Dissertations / Theses: 'Biometrics based recognition'

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Fouad, Marwa. "Towards Template Security for Iris-based Biometric Systems." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/22736.

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Personal identity refers to a set of attributes (e.g., name, social insurance number, etc.) that are associated with a person. Identity management is the process of creating, maintaining and destroying identities of individuals in a population. Biometric technologies are technologies developed to use statistical analysis of an individual’s biological or behavioral traits to determine his identity. Biometrics based authentication systems offer a reliable solution for identity management, because of their uniqueness, relative stability over time and security (among other reasons). Public acceptance of biometric systems will depend on their ability to ensure robustness, accuracy and security. Although robustness and accuracy of such systems are rapidly improving, there still remain some issues of security and balancing it with privacy. While the uniqueness of biometric traits offers a convenient and reliable means of identification, it also poses the risk of unauthorized cross-referencing among databases using the same biometric trait. There is also a high risk in case of a biometric database being compromised, since it’s not possible to revoke the biometric trait and re-issue a new one as is the case with passwords and smart keys. This unique attribute of biometric based authentication system poses a challenge that might slow down public acceptance and the use of biometrics for authentication purposes in large scale applications. In this research we investigate the vulnerabilities of biometric systems focusing on template security in iris-based biometric recognition systems. The iris has been well studied for authentication purposes and has been proven accurate in large scale applications in several airports and border crossings around the world. The most widely accepted iris recognition systems are based on Daugman’s model that creates a binary iris template. In this research we develop different systems using watermarking, bio-cryptography as well as feature transformation to achieve revocability and security of binary templates in iris based biometric authentication systems, while maintaining the performance that enables widespread application of these systems. All algorithms developed in this research are applicable on already existing biometric authentication systems and do not require redesign of these existing, well established iris-based authentication systems that use binary templates.

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Mohamed, Suliman M. "Fingerprint-based biometric recognition allied to fuzzy-neural feature classification." Thesis, Sheffield Hallam University, 2002. http://shura.shu.ac.uk/20071/.

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The research investigates fingerprint recognition as one of the most reliable biometrics identification methods. An automatic identification process of humans-based on fingerprints requires the input fingerprint to be matched with a large number of fingerprints in a database. To reduce the search time and computational complexity, it is desirable to classify the database of fingerprints into an accurate and consistent manner so that the input fingerprint is matched only with a subset of the fingerprints in the database. In this regard, the research addressed fingerprint classification. The goal is to improve the accuracy and speed up of existing automatic fingerprint identification algorithms. The investigation is based on analysis of fingerprint characteristics and feature classification using neural network and fuzzy-neural classifiers. The methodology developed, is comprised of image processing, computation of a directional field image, singular-point detection, and feature vector encoding. The statistical distribution of feature vectors was analysed using SPSS. Three types of classifiers, namely, multi-layered perceptrons, radial basis function and fuzzy-neural methods were implemented. The developed classification systems were tested and evaluated on 4,000 fingerprint images on the NIST-4 database. For the five-class problem, classification accuracy of 96.2% for FNN, 96.07% for MLP and 84.54% for RBF was achieved, without any rejection. FNN and MLP classification results are significant in comparison with existing studies, which have been reviewed.

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Cadavid, Steven. "Human Identification Based on Three-Dimensional Ear and Face Models." Scholarly Repository, 2011. http://scholarlyrepository.miami.edu/oa_dissertations/516.

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We propose three biometric systems for performing 1) Multi-modal Three-Dimensional (3D) ear + Two-Dimensional (2D) face recognition, 2) 3D face recognition, and 3) hybrid 3D ear recognition combining local and holistic features. For the 3D ear component of the multi-modal system, uncalibrated video sequences are utilized to recover the 3D ear structure of each subject within a database. For a given subject, a series of frames is extracted from a video sequence and the Region-of-Interest (ROI) in each frame is independently reconstructed in 3D using Shape from Shading (SFS). A ﬁdelity measure is then employed to determine the model that most accurately represents the 3D structure of the subject’s ear. Shape matching between a probe and gallery ear model is performed using the Iterative Closest Point (ICP) algorithm. For the 2D face component, a set of facial landmarks is extracted from frontal facial images using the Active Shape Model (ASM) technique. Then, the responses of the facial images to a series of Gabor ﬁlters at the locations of the facial landmarks are calculated. The Gabor features are stored in the database as the face model for recognition. Match-score level fusion is employed to combine the match scores obtained from both the ear and face modalities. The aim of the proposed system is to demonstrate the superior performance that can be achieved by combining the 3D ear and 2D face modalities over either modality employed independently. For the 3D face recognition system, we employ an Adaboost algorithm to builda classiﬁer based on geodesic distance features. Firstly, a generic face model is ﬁnely conformed to each face model contained within a 3D face dataset. Secondly, the geodesic distance between anatomical point pairs are computed across each conformed generic model using the Fast Marching Method. The Adaboost algorithm then generates a strong classiﬁer based on a collection of geodesic distances that are most discriminative for face recognition. The identiﬁcation and veriﬁcation performances of three Adaboost algorithms, namely, the original Adaboost algorithm proposed by Freund and Schapire, and two variants – the Gentle and Modest Adaboost algorithms – are compared. For the hybrid 3D ear recognition system, we propose a method to combine local and holistic ear surface features in a computationally eﬃcient manner. The system is comprised of four primary components, namely, 1) ear image segmentation, 2) local feature extraction and matching, 3) holistic feature extraction and matching, and 4) a fusion framework combining local and holistic features at the match score level. For the segmentation component, we employ our method proposed in [111], to localize a rectangular region containing the ear. For the local feature extraction and representation component, we extend the Histogram of Categorized Shapes (HCS) feature descriptor, proposed in [111], to an object-centered 3D shape descriptor, termed Surface Patch Histogram of Indexed Shapes (SPHIS), for surface patch representation and matching. For the holistic matching component, we introduce a voxelization scheme for holistic ear representation from which an eﬃcient, element-wise comparison of gallery-probe model pairs can be made. The match scores obtained from both the local and holistic matching components are fused to generate the ﬁnal match scores. Experimental results conducted on the University of Notre Dame (UND) collection J2 dataset demonstrate that theproposed approach outperforms state-of-the-art 3D ear biometric systems in both accuracy and eﬃciency.

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Ibrahim, Mina Ibrahim Samaan. "Wavelet based approaches for detection and recognition in ear biometrics." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/340675/.

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One of the most recent trends in biometrics is recognition by ear appearance in head profile images. Ear localization to determine the region of interest containing ears is an important step in an ear biometric system. To this end, we propose a robust, simple and effective method for ear detection from profile images by employing a bank of curved and stretched Gabor wavelets, known as banana wavelets. Our analysis shows that the banana wavelets demonstrate better performance than Gabor wavelets technique for ear localization. This indicates that the curved wavelets are advantageous for the detection of curved structures such as ears. This ear detection technique is fully automated, has encouraging performance and appears to be robust to degradation by noise. Addition of a preprocessing stage, based on skin detection using colour and texture, can improve the detection results even further. For recognition, we convolve the banana wavelets with an ear image and then apply local binary pattern (LBP) for texture analysis to the convolved image. The LBP histograms of the produced image are then used as features to describe an ear. A histogram intersection technique is then applied on the LBP histograms of two ears to measure their similarity for recognition. Analysis of variance is also exploited here to select features to identify the best banana filters for the recognition process. We show that the new banana wavelets, in combination with other analysis, can be used to achieve recognition by the ear, with practical advantages. The analyses focus particularly in simulating addition of noise and occlusion to a standard database, and their evaluation on a newer and much more demanding ear database. We also present an experimental study to investigate the effect of time difference between image acquisition for gallery and probe on the performance of ear recognition. This experimental research is the first study on the effect of time on ear biometrics and show that the recognition rate remains unchanged over time, confirming another advantage of deploying the human ear as a biometric.

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El, Seuofi Sherif M. "Performance Evaluation of Face Recognition Using Frames of Ten Pose Angles." Youngstown State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1198184813.

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Malavé, Laura Helena. "Silhouette based Gait Recognition: Research Resource and Limits." Scholar Commons, 2003. https://scholarcommons.usf.edu/etd/1423.

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As is seen from the work on gait recognition, there is a de-facto consensus about the silhouette of a person being the low-level representation of choice. It has been hypothesized that the performance degradation that is observed when one compares sequences taken on different surfaces, hence against different backgrounds, or when one considers outdoor sequences is due to the low silhouette quality and its variation. If only one can get better silhouettes the perfomance of gait recognition would be high. This thesis challenges that hypothesis. In the context of the HumanID Gait Challenge problem, we constructed a set of ground truth silhouttes over one gait cycles for 71 subjects, to test recognition across two conditions, shoe and surface. Using these, we show that the performance with ground truth silhouette is as good as that obtained by those obtained by a basic background subtraction algorithm. Therefore further research into ways to enhance silhouette extraction does not appear to be the most productive way to advance gait recognition. We also show, using the manually specified part level silhouettes, that most of the gait recognition power lies in the legs and the arms. The recognition power in various static gait recognition factors as extracted from a single view image, such as gait period, cadence, body size, height, leg size, and torso length, does not seem to be adequate. Using cummulative silhouette error images, we also suggest that gait actually changes when one changes walking surface; in particular the swing phase of the gait gets effected the most.

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Konuk, Baris. "Palmprint Recognition Based On 2-d Gabor Filters." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608138/index.pdf.

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In this thesis work, a detailed analysis of biometric technologies has been done and a new palmprint recognition algorithm has been implemented. The proposed algorithm is based on 2-D Gabor filters. The developed algorithm is first tested on The Hong Kong Polytechnic University Palmprint Database in terms of accuracy, speed and template size. Then a scanner is integrated into the developed algorithm in order to acquire palm images
in this way an online palmprint recognition system has been developed. Then a small palmprint database is formed via this system in Middle East Technical University. Results on this new database have also shown the success of the developed algorithm.

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Fons, Lluís Mariano. "Hardware accelerators for embedded fingerprint-based personal recognition systems." Doctoral thesis, Universitat Rovira i Virgili, 2012. http://hdl.handle.net/10803/83493.

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Abstract The development of automatic biometrics-based personal recognition systems is a reality in the current technological age. Not only those operations demanding stringent security levels but also many daily use consumer applications request the existence of computational platforms in charge of recognizing the identity of one individual based on the analysis of his/her physiological and/or behavioural characteristics. The state of the art points out two main open problems in the implementation of such applications: on the one hand, the needed reliability improvement in terms of recognition accuracy, overall security and real-time performances; and on the other hand, the cost reduction of those physical platforms in charge of the processing. This work aims at finding the proper system architecture able to address those limitations of current personal recognition applications. Embedded system solutions based on hardware-software co-design techniques and programmable (and run-time reconfigurable) logic devices under FPGAs or SOPCs is proven to be an efficient alternative to those existing multiprocessor systems based on HPCs, GPUs or PC platforms in the development of that kind of high-performance applications at low cost
El desenvolupament de sistemes automàtics de reconeixement personal basats en tècniques biomètriques esdevé una realitat en l’era tecnològica actual. No només aquelles operacions que exigeixen un elevat nivell de seguretat sinó també moltes aplicacions quotidianes demanen l’existència de plataformes computacionals encarregades de reconèixer la identitat d’un individu a partir de l’anàlisi de les seves característiques fisiològiques i/o comportamentals. L’estat de l’art de la tècnica identifica dues limitacions importants en la implementació d’aquest tipus d’aplicacions: per una banda, és necessària la millora de la fiabilitat d’aquests sistemes en termes de precisió en el procés de reconeixement personal, seguretat i execució en temps real; i per altra banda, és necessari reduir notablement el cost dels sistemes electrònics encarregats del processat biomètric. Aquest treball té per objectiu la cerca de l’arquitectura adequada a nivell de sistema que permeti fer front a les limitacions de les aplicacions de reconeixement personal actuals. Es demostra que la proposta de sistemes empotrats basats en tècniques de codisseny hardware-software i dispositius lògics programables (i reconfigurables en temps d’execució) sobre FPGAs o SOPCs resulta ser una alternativa eficient en front d’aquells sistemes multiprocessadors existents basats en HPCs, GPUs o plataformes PC per al desenvolupament d’aquests tipus d’aplicacions que requereixen un alt nivell de prestacions a baix cost.
El desarrollo de sistemas automáticos de reconocimiento personal basados en técnicas biométricas se ha convertido en una realidad en la era tecnológica actual. No tan solo aquellas operaciones que requieren un alto nivel de seguridad sino también muchas otras aplicaciones cotidianas exigen la existencia de plataformas computacionales encargadas de verificar la identidad de un individuo a partir del análisis de sus características fisiológicas y/o comportamentales. El estado del arte de la técnica identifica dos limitaciones importantes en la implementación de este tipo de aplicaciones: por un lado, es necesario mejorar la fiabilidad que presentan estos sistemas en términos de precisión en el proceso de reconocimiento personal, seguridad y ejecución en tiempo real; y por otro lado, es necesario reducir notablemente el coste de los sistemas electrónicos encargados de dicho procesado biométrico. Este trabajo tiene por objetivo la búsqueda de aquella arquitectura adecuada a nivel de sistema que permita hacer frente a las limitaciones de los sistemas de reconocimiento personal actuales. Se demuestra que la propuesta basada en sistemas embebidos implementados mediante técnicas de codiseño hardware-software y dispositivos lógicos programables (y reconfigurables en tiempo de ejecución) sobre FPGAs o SOPCs resulta ser una alternativa eficiente frente a aquellos sistemas multiprocesador actuales basados en HPCs, GPUs o plataformas PC en el ámbito del desarrollo de aplicaciones que demandan un alto nivel de prestaciones a bajo coste

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Kaloorazi, Maboud Farzaneh. "3D Ear Recognition Based on Force Field Transform." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4891.

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Reducing the dimensionality of the original pattern space in a definition of feature space while maintaining discriminatory power for classification is a general goal in pattern recognition. To accomplish this goal in the area of ear biometrics a highly recognized work was proposed by D. Hurley in 2D space. We were inspired by his work and developed a new method for 3D data. In a different way to Hurley’s work we obtain a potential energy surface from 3D depth image which underlies the force field and associated vector field has its own characteristics. Our feature extraction is conducted by combining two different approaches; an algorithmic approach as well as an analytical approach, both are based on the vector force field and geometrical approach which is based on 3D ear surface. To validate the technique, the ICP algorithm is used. This work differs from Hurley’s work not only because of the algorithm, but also because of the nature of the 3D data which delivers topological information of the images. We exploit geometry to acquire surface information of the ear which yields richer features than the original work. The performance of the proposed method was evaluated using the University of Notre Dame (UND) collection J2 database and MATLAB has been used as the software package.
phone number: 0700099273

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Liu, Zongyi. "Gait-Based Recognition at a Distance: Performance, Covariate Impact and Solutions." Scholar Commons, 2004. https://scholarcommons.usf.edu/etd/1134.

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It has been noticed for a long time that humans can identify others based on their biological movement from a distance. However, it is only recently that computer vision based gait biometrics has received much attention. In this dissertation, we perform a thorough study of gait recognition from a computer vision perspective. We first present a parameterless baseline recognition algorithm, which bases similarity on spatio-temporal correlation that emphasizes gait dynamics as well as gait shapes. Our experiments are performed with three popular gait databases: the USF/NIST HumanID Gait Challenge outdoor database with 122 subjects, the UMD outdoor database with 55 subjects, and the CMU Mobo indoor database with 25 subjects. Despite its simplicity, the baseline algorithm shows strong recognition power. On the other hand, the outcome suggests that changes in surface and time have strong impact on recognition with significant drop in performance. To gain insight into the effects of image segmentation on recognition -- a possible cause for performance degradation, we propose a silhouette reconstruction method based on a Population Hidden Markov Model (pHMM), which models gait over one cycle, coupled with an Eigen-stance model utilizing the Principle Component Analysis (PCA) of the silhouette shapes. Both models are built from a set of manually created silhouettes of 71 subjects. Given a sequence of machine segmented silhouettes, each frame is matched into a stance by pHMM using the Viterbi algorithm, and then is projected into and reconstructed by the Eigen-stance model. We demonstrate that the system dramatically improves the silhouette quality. Nonetheless, it does little help for recognition, indicating that segmentation is not the key factor of the covariate impacts. To improve performance, we look into other aspects. Toward this end, we propose three recognition algorithms: (i) an averaged silhouette based algorithm that deemphasizes gait dynamics, which substantially reduces computation time but achieves similar recognition power with the baseline algorithm; (ii) an algorithm that normalizes gait dynamics using pHMM and then uses Euclidean distance between corresponding selected stances -- this improves recognition over surface and time; and (iii) an algorithm that also performs gait dynamics normalization using pHMM, but instead of Euclidean distances, we consider distances in shape space based on the Linear Discriminant Analysis (LDA) and consider measures that are invariant to morphological deformation of silhouettes. This algorithm statistically improves the recognition over all covariates. Compared with the best reported algorithm to date, it improves the top-rank identification rate (gallery size: 122 subjects) for comparison across hard covariates: briefcase, surface type and time, by 22%, 14%, and 12% respectively. In addition to better gait algorithms, we also study multi-biometrics combination to improve outdoor biometric performance, specifically, fusing with face data. We choose outdoor face recognition, a "known" hard problem in face biometrics, and test four combination schemes: score sum, Bayesian rule, confidence score sum, and rank sum. We find that the recognition power after combination is significantly stronger although individual biometrics are weak, suggesting another effective approach to improve biometric recognition. The fundamental contributions of this work include (i) establishing the "hard" problems for gait recognition involving comparison across time, surface, and briefcase carrying conditions, (ii) revealing that their impacts cannot be explained by silhouette segmentation, (iii) demonstrating that gait shape is more important than gait dynamics in recognition, and (iv) proposing a novel gait algorithm that outperforms other gait algorithms to date.

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Alejo, Willy, Daniel Rodriguez, Guillermo Kemper, and Universidad Peruana de Ciencias Aplicadas (UPC). "A biometric method based on the matching of dilated and skeletonized IR images of the veins map of the dorsum of the hand." IEEE, 2015. http://hdl.handle.net/10757/556175.

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Article
This work proposes a biometric identification system that works together with a palm vein reader sensor and a hand-clenching support, designed to perform the capture the back of the hand. Several processing steps were performed: extraction of the region of interest, binarization, dilation, noise filtering, skeletonization, as well as extraction and verification of patterns based on the measurment of coincidence of vertical and horizontal displacements of skeletonized and dilated images. The proposed method achieved the following results: processing time post capture of 1.8 seconds, FRR of 0.47% and FAR of 0,00%, with a referential database of 50 people from a total of 1500 random captures.

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Mohanty, Pranab. "Learning from biometric distances : performance and security related issues in face recognition systems." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002298.

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Komulainen, J. (Jukka). "Software-based countermeasures to 2D facial spoofing attacks." Doctoral thesis, Oulun yliopisto, 2015. http://urn.fi/urn:isbn:9789526208732.

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Abstract Because of its natural and non-intrusive interaction, identity verification and recognition using facial information is among the most active areas in computer vision research. Unfortunately, it has been shown that conventional 2D face recognition techniques are vulnerable to spoofing attacks, where a person tries to masquerade as another one by falsifying biometric data and thereby gaining an illegitimate advantage. This thesis explores different directions for software-based face anti-spoofing. The proposed approaches are divided into two categories: first, low-level feature descriptors are applied for describing the static and dynamic characteristic differences between genuine faces and fake ones in general, and second, complementary attack-specific countermeasures are investigated in order to overcome the limitations of generic spoof detection schemes. The static face representation is based on a set of well-known feature descriptors, including local binary patterns, Gabor wavelet features and histogram of oriented gradients. The key idea is to capture the differences in quality, light reflection and shading by analysing the texture and gradient structure of the input face images. The approach is then extended to the spatiotemporal domain when both facial appearance and dynamics are exploited for spoof detection using local binary patterns from three orthogonal planes. It is reasonable to assume that no generic spoof detection scheme is able to detect all known, let alone unseen, attacks scenarios. In order to find out well-generalizing countermeasures, the problem of anti-spoofing is broken into two attack-specific sub-problems based on whether the spoofing medium can be detected in the provided view or not. The spoofing medium detection is performed by describing the discontinuities in the gradient structures around the detected face. If the display medium is concealed outside the view, a combination of face and background motion correlation measurement and texture analysis is applied. Furthermore, an open-source anti-spoofing fusion framework is introduced and its system-level performance is investigated more closely in order to gain insight on how to combine different anti-spoofing modules. The proposed spoof detection schemes are evaluated on the latest benchmark datasets. The main findings of the experiments are discussed in the thesis
Tiivistelmä Kasvokuvaan perustuvan henkilöllisyyden tunnistamisen etuja ovat luonnollinen vuorovaikutus ja etätunnistus, minkä takia aihe on ollut erittäin aktiivinen tutkimusalue konenäön tutkimuksessa. Valitettavasti tavanomaiset kasvontunnistustekniikat ovat osoittautuneet haavoittuvaisiksi hyökkäyksille, joissa kameralle esitetään jäljennös kohdehenkilön kasvoista positiivisen tunnistuksen toivossa. Tässä väitöskirjassa tutkitaan erilaisia ohjelmistopohjaisia ratkaisuja keinotekoisten kasvojen ilmaisuun petkuttamisen estämiseksi. Työn ensimmäisessä osassa käytetään erilaisia matalan tason piirteitä kuvaamaan aitojen ja keinotekoisten kasvojen luontaisia staattisia ja dynaamisia eroavaisuuksia. Työn toisessa osassa esitetään toisiaan täydentäviä hyökkäystyyppikohtaisia vastakeinoja, jotta yleispätevien menetelmien puutteet voitaisiin ratkaista ongelmaa rajaamalla. Kasvojen staattisten ominaisuuksien esitys perustuu yleisesti tunnettuihin matalan tason piirteisiin, kuten paikallisiin binäärikuvioihin, Gabor-tekstuureihin ja suunnattujen gradienttien histogrammeihin. Pääajatuksena on kuvata aitojen ja keinotekoisten kasvojen laadun, heijastumisen ja varjostumisen eroavaisuuksia tekstuuria ja gradienttirakenteita analysoimalla. Lähestymistapaa laajennetaan myös tila-aika-avaruuteen, jolloin hyödynnetään samanaikaisesti sekä kasvojen ulkonäköä ja dynamiikkaa irroittamalla paikallisia binäärikuvioita tila-aika-avaruuden kolmelta ortogonaaliselta tasolta. Voidaan olettaa, ettei ole olemassa yksittäistä yleispätevää vastakeinoa, joka kykenee ilmaisemaan jokaisen tunnetun hyökkäystyypin, saati tuntemattoman. Näin ollen työssä keskitytään tarkemmin kahteen hyökkäystilanteeseen. Ensimmäisessä tapauksessa huijausapuvälineen reunoja ilmaistaan analysoimalla gradienttirakenteiden epäjatkuvuuksia havaittujen kasvojen ympäristössä. Jos apuvälineen reunat on piilotettu kameran näkymän ulkopuolelle, petkuttamisen ilmaisu toteutetaan yhdistämällä kasvojen ja taustan liikkeen korrelaation mittausta ja kasvojen tekstuurianalyysiä. Lisäksi työssä esitellään vastakeinojen yhdistämiseen avoimen lähdekoodin ohjelmisto, jonka avulla tutkitaan lähemmin menetelmien fuusion vaikutuksia. Tutkimuksessa esitetyt menetelmät on kokeellisesti vahvistettu alan viimeisimmillä julkisesti saatavilla olevilla tietokannoilla. Tässä väitöskirjassa käydään läpi kokeiden päähavainnot

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Phang, Shiau Shing. "Investigating and developing a model for iris changes under varied lighting conditions." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16672/1/Shiau_Shing_Phang_Thesis.pdf.

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Biometric identification systems have several distinct advantages over other authentication technologies, such as passwords, in reliably recognising individuals. Iris based recognition is one such biometric recognition system. Unlike other biometrics such as fingerprints or face images, the distinct aspect of the iris comes from its randomly distributed features. The patterns of these randomly distributed features on the iris have been proved to be fixed in a person's lifetime, and are stable over time for healthy eyes except for the distortions caused by the constriction and dilation of the pupil. The distortion of the iris pattern caused by pupillary activity, which is mainly due changes in ambient lighting conditions, can be significant. One important question that arises from this is: How closely do two different iris images of the same person, taken at different times using different cameras, in different environments, and under different lighting conditions, agree with each other? It is also problematic for iris recognition systems to correctly identify a person when his/her pupil size is very different from the person's iris images, used at the time of constructing the system's data-base. To date, researchers in the field of iris recognition have made attempts to address this problem, with varying degrees of success. However, there is still a need to conduct in-depth investigations into this matter in order to arrive at more reliable solutions. It is therefore necessary to study the behaviour of iris surface deformation caused by the change of lighting conditions. In this thesis, a study of the physiological behaviour of pupil size variation under different normal indoor lighting conditions (100 lux ~ 1,200 lux) and brightness levels is presented. The thesis also presents the results of applying Elastic Graph Matching (EGM) tracking techniques to study the mechanisms of iris surface deformation. A study of the pupil size variation under different normal indoor lighting conditions was conducted. The study showed that the behaviour of the pupil size can be significantly different from one person to another under the same lighting conditions. There was no evidence from this study to show that the exact pupil sizes of an individual can be determined at a given illumination level. However, the range of pupil sizes can be estimated for a range of specific lighting conditions. The range of average pupil sizes under normal indoor lighting found was between 3 mm and 4 mm. One of the advantages of using EGM for iris surface deformation tracking is that it incorporates the benefit of the use of Gabor wavelets to encode the iris features for tracking. The tracking results showed that the radial stretch of the iris surface is nonlinear. However, the amount of extension of iris surface at any point on the iris during the stretch is approximately linear. The analyses of the tracking results also showed that the behaviour of iris surface deformation is different from one person to another. This implies that a generalised iris surface deformation model cannot be established for personal identification. However, a deformation model can be established for every individual based on their analysis result, which can be useful for personal verification using the iris. Therefore, analysis of the tracking results of each individual was used to model iris surface deformations for that individual. The model was able to estimate the movement of a point on the iris surface at a particular pupil size. This makes it possible to estimate and construct the 2D deformed iris image of a desired pupil size from a given iris image of another different pupil size. The estimated deformed iris images were compared with their actual images for similarity, using an intensitybased (zero mean normalised cross-correlation). The result shows that 86% of the comparisons have over 65% similarity between the estimated and actual iris image. Preliminary tests of the estimated deformed iris images using an open-source iris recognition algorithm have showed an improved personal verification performance. The studies presented in this thesis were conducted using a very small sample of iris images and therefore should not be generalised, before further investigations are conducted.

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Phang, Shiau Shing. "Investigating and developing a model for iris changes under varied lighting conditions." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16672/.

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Biometric identification systems have several distinct advantages over other authentication technologies, such as passwords, in reliably recognising individuals. Iris based recognition is one such biometric recognition system. Unlike other biometrics such as fingerprints or face images, the distinct aspect of the iris comes from its randomly distributed features. The patterns of these randomly distributed features on the iris have been proved to be fixed in a person's lifetime, and are stable over time for healthy eyes except for the distortions caused by the constriction and dilation of the pupil. The distortion of the iris pattern caused by pupillary activity, which is mainly due changes in ambient lighting conditions, can be significant. One important question that arises from this is: How closely do two different iris images of the same person, taken at different times using different cameras, in different environments, and under different lighting conditions, agree with each other? It is also problematic for iris recognition systems to correctly identify a person when his/her pupil size is very different from the person's iris images, used at the time of constructing the system's data-base. To date, researchers in the field of iris recognition have made attempts to address this problem, with varying degrees of success. However, there is still a need to conduct in-depth investigations into this matter in order to arrive at more reliable solutions. It is therefore necessary to study the behaviour of iris surface deformation caused by the change of lighting conditions. In this thesis, a study of the physiological behaviour of pupil size variation under different normal indoor lighting conditions (100 lux ~ 1,200 lux) and brightness levels is presented. The thesis also presents the results of applying Elastic Graph Matching (EGM) tracking techniques to study the mechanisms of iris surface deformation. A study of the pupil size variation under different normal indoor lighting conditions was conducted. The study showed that the behaviour of the pupil size can be significantly different from one person to another under the same lighting conditions. There was no evidence from this study to show that the exact pupil sizes of an individual can be determined at a given illumination level. However, the range of pupil sizes can be estimated for a range of specific lighting conditions. The range of average pupil sizes under normal indoor lighting found was between 3 mm and 4 mm. One of the advantages of using EGM for iris surface deformation tracking is that it incorporates the benefit of the use of Gabor wavelets to encode the iris features for tracking. The tracking results showed that the radial stretch of the iris surface is nonlinear. However, the amount of extension of iris surface at any point on the iris during the stretch is approximately linear. The analyses of the tracking results also showed that the behaviour of iris surface deformation is different from one person to another. This implies that a generalised iris surface deformation model cannot be established for personal identification. However, a deformation model can be established for every individual based on their analysis result, which can be useful for personal verification using the iris. Therefore, analysis of the tracking results of each individual was used to model iris surface deformations for that individual. The model was able to estimate the movement of a point on the iris surface at a particular pupil size. This makes it possible to estimate and construct the 2D deformed iris image of a desired pupil size from a given iris image of another different pupil size. The estimated deformed iris images were compared with their actual images for similarity, using an intensitybased (zero mean normalised cross-correlation). The result shows that 86% of the comparisons have over 65% similarity between the estimated and actual iris image. Preliminary tests of the estimated deformed iris images using an open-source iris recognition algorithm have showed an improved personal verification performance. The studies presented in this thesis were conducted using a very small sample of iris images and therefore should not be generalised, before further investigations are conducted.

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Castelano, Célio Ricardo. "Estudo comparativo da transformada wavelet no reconhecimento de padrões da íris humana." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-30112006-134736/.

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Neste trabalho é apresentado um método para reconhecimento de seres humanos através da textura da íris. A imagem do olho é processada através da análise do gradiente, com uma técnica de dispersão aleatória de sementes. Um vetor de características é extraído para cada íris, baseado na análise dos componentes wavelet em diversos níveis de decomposição. Para se mensurar as distâncias entre esses vetores foi utilizado o cálculo da distância Euclidiana, gerando-se curvas recall x precision para se medir a eficiência do método desenvolvido. Os resultados obtidos com algumas famílias wavelets demonstraram que o método proposto é capaz de realizar o reconhecimento humano através da íris com uma precisão eficiente.
This work presents a method for recognition of human beings by iris texture. The image of the eye is processed through gradient analysis, based on a random dispersion of seeds. So, it is extracted a feature vector for each iris based on wavelet transform in some levels of decomposition. To estimate the distances between these vectors it was used the Euclidean distance, and recall x precision curves are generated to measure the efficiency of the developed method. The results gotten with some wavelets families had demonstrated that the proposed methodology is capable to do human recognition through the iris with an efficient precision.

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Font, Aragonès Xavier. "Visible, near infrared and thermal hand-based image biometric recognition." Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/117685.

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Biometric Recognition refers to the automatic identification of a person based on his or her anatomical characteristic or modality (i.e., fingerprint, palmprint, face) or behavioural (i.e., signature) characteristic. It is a fundamental key issue in any process concerned with security, shared resources, network transactions among many others. Arises as a fundamental problem widely known as recognition, and becomes a must step before permission is granted. It is supposed that protects key resources by only allowing those resources to be used by users that have been granted authority to use or to have access to them. Biometric systems can operate in verification mode, where the question to be solved is Am I who I claim I am? or in identification mode where the question is Who am I? Scientific community has increased its efforts in order to improve performance of biometric systems. Depending on the application many solutions go in the way of working with several modalities or combining different classification methods. Since increasing modalities require some user inconvenience many of these approaches will never reach the market. For example working with iris, face and fingerprints requires some user effort in order to help acquisition. This thesis addresses hand-based biometric system in a thorough way. The main contributions are in the direction of a new multi-spectral hand-based image database and methods for performance improvement. The main contributions are: A) The first multi-spectral hand-based image database from both hand faces: palmar and dorsal. Biometric database are a precious commodity for research, mainly when it offers something new like visual (VIS), near infrared (NIR) and thermography (TIR) images at a time. This database with a length of 100 users and 10 samples per user constitute a good starting point to check algorithms and hand suitability for recognition. B) In order to correctly deal with raw hand data, some image preprocessing steps are necessary. Three different segmentation phases are deployed to deal with VIS, NIR and TIR images specifically. Some of the tough questions to address: overexposed images, ring fingers and the cuffs, cold finger and noise image. Once image segmented, two different approaches are prepared to deal with the segmented data. These two approaches called: Holistic and Geometric define the main focus to extract the feature vector. These feature vectors can be used alone or can be combined in some way. Many questions can be stated: e.g. which approach is better for recognition?, Can fingers alone obtain better performance than the whole hand? and Is thermography hand information suitable for recognition due to its thermoregulation properties? A complete set of data ready to analyse, coming from the holistic and geometric approach have been designed and saved to test. Some innovative geometric approach related to curvature will be demonstrated. C) Finally the Biometric Dispersion Matcher (BDM) is used in order to explore how it works under different fusion schemes, as well as with different classification methods. It is the intention of this research to contrast what happen when using other methods close to BDM like Linear Discriminant Analysis (LDA). At this point, some interesting questions will be solved, e.g. by taking advantage of the finger segmentation (as five different modalities) to figure out if they can outperform what the whole hand data can teach us.
El Reconeixement Biomètric fa referència a la identi cació automàtica de persones fent us d'alguna característica o modalitat anatòmica (empremta digital) o d'alguna característica de comportament (signatura). És un aspecte fonamental en qualsevol procés relacionat amb la seguretat, la compartició de recursos o les transaccions electròniques entre d'altres. És converteix en un pas imprescindible abans de concedir l'autorització. Aquesta autorització, s'entén que protegeix recursos clau, permeten així, que aquests siguin utilitzats pels usuaris que han estat autoritzats a utilitzar-los o a tenir-hi accés. Els sistemes biomètrics poden funcionar en veri cació, on es resol la pregunta: Soc jo qui dic que soc? O en identi cació on es resol la qüestió: Qui soc jo? La comunitat cientí ca ha incrementat els seus esforços per millorar el rendiment dels sistemes biomètrics. En funció de l'aplicació, diverses solucions s'adrecen a treballar amb múltiples modalitats o combinant diferents mètodes de classi cació. Donat que incrementar el número de modalitats, representa a la vegada problemes pels usuaris, moltes d'aquestes aproximacions no arriben mai al mercat. La tesis contribueix principalment en tres grans àrees, totes elles amb el denominador comú següent: Reconeixement biometric a través de les mans. i) La primera d'elles constitueix la base de qualsevol estudi, les dades. Per poder interpretar, i establir un sistema de reconeixement biomètric prou robust amb un clar enfocament a múltiples fonts d'informació, però amb el mínim esforç per part de l'usuari es construeix aquesta Base de Dades de mans multi espectral. Les bases de dades biomètriques constitueixen un recurs molt preuat per a la recerca; sobretot si ofereixen algun element nou com es el cas. Imatges de mans en diferents espectres electromagnètics: en visible (VIS), en infraroig (NIR) i en tèrmic (TIR). Amb un total de 100 usuaris, i 10 mostres per usuari, constitueix un bon punt de partida per estudiar i posar a prova sistemes multi biomètrics enfocats a les mans. ii) El segon bloc s'adreça a les dues aproximacions existents en la literatura per a tractar les dades en brut. Aquestes dues aproximacions, anomenades Holística (tracta la imatge com un tot) i Geomètrica (utilitza càlculs geomètrics) de neixen el focus alhora d'extreure el vector de característiques. Abans de tractar alguna d'aquestes dues aproximacions, però, és necessària l'aplicació de diferents tècniques de preprocessat digital de la imatge per obtenir les regions d'interès desitjades. Diferents problemes presents a les imatges s'han hagut de solucionar de forma original per a cadascuna de les tipologies de les imatges presents: VIS, NIR i TIR. VIS: imatges sobre exposades, anells, mànigues, braçalets. NIR: Ungles pintades, distorsió en forma de soroll en les imatges TIR: Dits freds La segona àrea presenta aspectes innovadors, ja que a part de segmentar la imatge de la ma, es segmenten tots i cadascun dels dits (feature-based approach). Així aconseguim contrastar la seva capacitat de reconeixement envers la ma de forma completa. Addicionalment es presenta un conjunt de procediments geomètrics amb la idea de comparar-los amb els provinents de l'extracció holística. La tercera i última àrea contrasta el procediment de classi cació anomenat Biometric Dispersion Matcher (BDM) amb diferents situacions. La primera relacionada amb l'efectivitat respecte d'altres mètode de reconeixement, com ara l'Anàlisi Lineal Discriminant (LDA) o bé mètodes com KNN o la regressió logística. Les altres situacions que s'analitzen tenen a veure amb múltiples fonts d'informació, quan s'apliquen tècniques de normalització i/o estratègies de combinació (fusió) per millorar els resultats. Els resultats obtinguts no deixen lloc per a la confusió, i són certament prometedors en el sentit que posen a la llum la importància de combinar informació complementària per obtenir rendiments superiors.

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Datta, Ankur. "Gait Based Recognition." Honors in the Major Thesis, University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/436.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf
Bachelors
Engineering and Computer Science
Computer Science

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19

Manohar, Vasant. "Video-Based Person Identification Using Facial Strain Maps as a Biometric." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/3797.

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Research on video-based face recognition has started getting increased attention in the past few years. Algorithms developed for video have an advantage from the availability of plentitude of frames in videos to extract information from. Despite this fact, most research in this direction has limited the scope of the problem to the application of still image-based approaches to some selected frames on which 2D algorithms are expected to perform well. It can be realized that such an approach only uses the spatial information contained in video and does not incorporate the temporal structure.Only recently has the intelligence community begun to approach the problem in this direction. Video-based face recognition algorithms in the last couple of years attempt to simultaneously use the spatial and temporal information for the recognition of moving faces. A new face recognition method that falls into the category of algorithms that adopt spatio-temporal representation and utilizes dynamic information extracted from video is presented. The method was designed based on the hypothesis that the strain pattern exhibited during facial expression provides a unique "fingerprint" for recognition. First, a dense motion field is obtained with an optical flow algorithm. A strain pattern is then derived from the motion field. In experiments with 30 subjects, results indicate that strain pattern is an useful biometric, especially when dealing with extreme conditions such as shadow light and face camouflage, for which conventional face recognition methods are expected to fail. The ability to characterize the face using the elastic properties of facial skin opens up newer avenues to the face recognition community in the context of modeling a face using features beyond visible cues.

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Nickel, Claudia [Verfasser], Johannes [Akademischer Betreuer] Buchmann, and Christoph [Akademischer Betreuer] Busch. "Accelerometer-based Biometric Gait Recognition for Authentication on Smartphones / Claudia Nickel. Betreuer: Johannes Buchmann ; Christoph Busch." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2012. http://d-nb.info/1106116119/34.

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Jardini, Evandro de Araújo. "MFIS: algoritmo de reconhecimento e indexação em base de dados de impressões digitais em espaço métrico." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-04042008-143239/.

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O problema dos métodos tradicionais de identificação de pessoas é que são baseados em senhas e assim podem ser esquecidas, roubadas, perdidas, copiadas, armazenadas de maneira insegura e até utilizadas por uma pessoa que não tenha autorização. Os sistemas biométricos automáticos surgiram para oferecer uma alternativa para o reconhecimento de pessoas com maior segurança e eficiência. Uma das técnicas biométricas mais utilizadas é o reconhecimento de impressões digitais. Com o aumento do uso de impressões digitais nestes sistemas, houve o surgimento de grandes bancos de dados de impressões digitais, tornado-se um desafio encontrar a melhor e mais rápida maneira de recuperar informações. De acordo com os desafios apresentados, este trabalho tem duas propostas: i) desenvolver um novo algoritmo métrico para identificação de impressões digitais e ii) usá-lo para indexar um banco de dados de impressões digitais através de uma árvore de busca métrica. Para comprovar a eficiência do algoritmo desenvolvido foram realizados testes sobre duas bases de imagens de impressões digitais, disponibilizadas no evento Fingerprint Verification Competition dos anos de 2000 e 2002. Os resultados obtidos foram comparados com os resultados do algoritmo proposto por Bozorth. A avaliação dos resultados foi feita pela curva Receiver Operating Characteristic juntamente com a taxa de Equal Error Rate, sendo que, o método proposto, obteve a taxa de 4,9% contra 7,2% do método de Bozorth e de 2,0% contra 2,7% do Bozorth nos banco de dados dos anos de 2000 e 2002 respectivamente. Nos testes de robustez, o algoritmo proposto conseguiu identificar uma impressão digital com uma parte da imagem de apenas 30% do tamanho original e por se utilizar uma base de dados indexada, o mesmo obteve vantagens de tempo na recuperação de pequenas quantidades de impressões digitais de uma mesma classe.
The problem of the traditional methods of people identification is that they are based on passwords which may to be forgotten, stolen, lost, copied, stored in an insecure way and be used by unauthorized person. Automatic biometric systems appeared to provide an alternative for the recognition of people in a more safe and efficienty way. One most biometrics techniques used is the fingerprint recognition. With the increasing use of fingerprints in biometric systems, large fingerprint databases emerged, and with them, the challenge to find the best and fastest way to recover informations. According to the challenges previously mentioned, this work presents two proposals: i) to develop a newmetric algorithm for the identification of fingerprints and ii) to use it to index a fingerprint database using a metric search tree. To prove the efficiency of the developed algorithm tests were performed on two fingerprint images databases from Fingerprint Verification Competition of years 2000 and 2002. The obtained results were compared to the results of the algorithm proposed by Bozorth and was evaluated by the Receiver Operating Characteristic curve and the Equal Error Rate, where the proposed method is of 4.9% against 7.2% of Bozorth and 2.0% of the algorithm proposed against 2.7% of the Bozorth in the databases of the yearsof 2000 and 2002. In the robustness tests, the proposed algorithm as able to identify a fingerprint with only 30% of the original size and when using an a indexed database, it obtained better performance in the recovery of small amounts of fingerprints of a single class.

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Assaad, Firas Souhail. "Biometric Multi-modal User Authentication System based on Ensemble Classifier." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1418074931.

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Chidambaram, Chidambaram. "A contribution for single and multiple faces recognition using feature-based approaches." Universidade Tecnológica Federal do Paraná, 2013. http://repositorio.utfpr.edu.br/jspui/handle/1/715.

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Entre os sistemas de reconhecimento biométrico, a biometria da face exerce um papel importante nas atividades de pesquisa e nas aplicações de segurança, pois a face pode ser obtida sem conhecimento prévio de um indivíduo. Atualmente, uma grande quantidade de imagens digitais e seqüências de vídeo têm sido adquiridas principalmente sob condições não-controladas, freqüentemente com ruído, borramento, oclusão e variação de escala e iluminação. Por esses problemas, o reconhecimento facial (RF) é ainda considerado como uma área de pesquisa ativa e uma tarefa desafiadora. A motivação vem do fato que o reconhecimento de faces nas imagens com fundo complexo e em base de imagens faciais tem sido uma aplicação de sucesso. Portanto, o principal foco deste trabalho é reconhecer uma ou mais faces em imagens estáticas contendo diversos indivíduos e um individuo (face) em uma base de imagens com faces únicas obtidas sob condições diferentes. Para trabalhar com faces múltiplas, uma abordagem semi-supervisionada foi proposta baseada em características locais invariantes e discriminativas. A extração de características (EC) locais é feita utilizando-se do algoritmo Speeded-Up Robust Features (SURF). A busca por regiões nas quais as características ótimas podem ser extraídas é atendida através do algoritmo ABC. Os resultados obtidos mostram que esta abordagem é robusta e eficiente para aplicações de RF exceto para faces com iluminação não-uniforme. Muitos trabalhos de RF são baseados somente na extração de uma característica e nas abordagens de aprendizagem de máquina. Além disso, as abordagens existentes de EC usam características globais e/ou locais. Para obter características relevantes e complementares, a metodologia de RF deve considerar também as características de diferentes tipos e semi-globais. Portanto, a abordagem hierárquica de RF é proposta baseada na EC como globais, semi-globais e locais. As globais e semi-globais são extraídas utilizando-se de Color Angles (CA) e Edge Histogram Descriptors (EHD) enquanto somente características locais são extraídas utilizando-se do SURF. Uma ampla análise experimental foi feita utilizando os três métodos individualmente, seguido por um esquema hierárquico de três - estágios usando imagens faciais obtidas sob duas condições diferentes de iluminação com expressão facial e uma variação de escala leve. Além disso, para CA e EHD, o desempenho da abordagem foi também analisado combinando-se características globais, semi-globais e locais. A abordagem proposta alcança uma taxa de reconhecimento alta com as imagens de todas as condições testadas neste trabalho. Os resultados enfatizam a influência das características locais e semi-globais no desempenho do reconhecimento. Em ambas as abordagens, tanto nas faces únicas quanto nas faces múltiplas, a conquista principal é o alto desempenho obtido somente com a capacidade discriminativa de características sem nenhum esquema de treinamento.
Among biometric recognition systems, face biometrics plays an important role in research activities and security applications since face images can be acquired without any knowledge of individuals. Nowadays a huge amount of digital images and video sequences have been acquired mainly from uncontrolled conditions, frequently including noise, blur, occlusion and variation on scale and illumination. Because of these issues, face recognition (FR) is still an active research area and becomes a complex problem and a challenging task. In this context, the motivation comes from the fact that recognition of faces in digital images with complex background and databases of face images have become one of the successful applications of Computer Vision. Hence, the main goal of this work is to recognize one or more faces from still images with multiple faces and from a database of single faces obtained under different conditions. To work with multiple face images under varying conditions, a semi-supervised approach proposed based on the invariant and discriminative power of local features. The extraction of local features is done using Speeded-Up Robust Features (SURF). The search for regions from which optimal features can be extracted is fulfilled by an improved ABC algorithm. To fully exploit the proposed approach, an extensive experimental analysis was performed. Results show that this approach is robust and efficient for face recognition applications except for faces with non-uniform illumination. In the literature, a significant number of single FR researches are based on extraction of only one feature and machine learning approaches. Besides, existing feature extraction approaches broadly use either global or local features. To obtain relevant and complementary features from face images, a face recognition methodology should consider heterogeneous features and semi-global features. Therefore, a novel hierarchical semi-supervised FR approach is proposed based on extraction of global, semi-global and local features. Global and semi-global features are extracted using Color Angles (CA) and edge histogram descriptors (EHD) meanwhile only local features are extracted using SURF. An extensive experimental analysis using the three feature extraction methods was done first individually followed by a three-stage hierarchical scheme using the face images obtained under two different lighting conditions with facial expression and slight scale variation. Furthermore, the performance of the approach was also analyzed using global, semi-global and local features combinations for CA and EHD. The proposed approach achieves high recognition rates considering all image conditions tested in this work. In addition to this, the results emphasize the influence of local and semi-global features in the recognition performance. In both, single face and multiple faces approaches, the main achievement is the high performance obtained only from the discriminative capacity of extracted features without any training schemes.

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Bekli, Zeid, and William Ouda. "A performance measurement of a Speaker Verification system based on a variance in data collection for Gaussian Mixture Model and Universal Background Model." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20122.

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Voice recognition has become a more focused and researched field in the last century,and new techniques to identify speech has been introduced. A part of voice recognition isspeaker verification which is divided into Front-end and Back-end. The first componentis the front-end or feature extraction where techniques such as Mel-Frequency CepstrumCoefficients (MFCC) is used to extract the speaker specific features of a speech signal,MFCC is mostly used because it is based on the known variations of the humans ear’scritical frequency bandwidth. The second component is the back-end and handles thespeaker modeling. The back-end is based on the Gaussian Mixture Model (GMM) andGaussian Mixture Model-Universal Background Model (GMM-UBM) methods forenrollment and verification of the specific speaker. In addition, normalization techniquessuch as Cepstral Means Subtraction (CMS) and feature warping is also used forrobustness against noise and distortion. In this paper, we are going to build a speakerverification system and experiment with a variance in the amount of training data for thetrue speaker model, and to evaluate the system performance. And further investigate thearea of security in a speaker verification system then two methods are compared (GMMand GMM-UBM) to experiment on which is more secure depending on the amount oftraining data available.This research will therefore give a contribution to how much data is really necessary fora secure system where the False Positive is as close to zero as possible, how will theamount of training data affect the False Negative (FN), and how does this differ betweenGMM and GMM-UBM.The result shows that an increase in speaker specific training data will increase theperformance of the system. However, too much training data has been proven to beunnecessary because the performance of the system will eventually reach its highest point and in this case it was around 48 min of data, and the results also show that the GMMUBM model containing 48- to 60 minutes outperformed the GMM models.

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Mendes, Wender Cabral. "Reconhecimento de pessoas pela marcha usando redução de dimensionalidade de contornos no domínio da frequência." Universidade Federal de Goiás, 2016. http://repositorio.bc.ufg.br/tede/handle/tede/5931.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Gait recognition via computer vision attracted increasing interest for its noninvasive characteristic and mainly for your advantage of recognizing people at distance. Recognition is performing extracting features included in gait, this features are extracted from images sequence of people walking. The main challenges of gait recognition is to extract characteristics with unique information for each person, in additional, the use of accessories and clothes difficult the feature extraction process. This paper proposes a gait recognition method using information of people’s contours transformed in domain frequence by Discrete Fourier Transform. A lot of data are generated from the contours, thereby, three different techniques for dimensionality reduction CDA (Class Discrimination Ability), PCA (Principal Component Analysis) and PLS (Partial Least Squares) are employed to reduce the dimensionality of data and generate characteristics that are relevant to the recongnition system. Two classifiers, KNN (K-Nearest Neighbor) and LDA (Linear Discriminant Analysis) classify the characteristics that are returned by the dimensionality reduction methods. The accuracy are achieved by the combination of the dimensionality reduction methods and classifiers, the highest accuracy was 92:67%, which was achieved with the combination between the LDA and PCA (LDAPCA). Therefore, the results show that the information contained in the contours of silhouette are discriminant to recognize people by their gait.
O reconhecimento de pessoas através da marcha humana via visão computacional tem ganhado destaque por ser uma técnica biométrica não invasiva e principalmente por sua vantagem de reconhecer pessoas à distância. O reconhecimento é realizando extraindo características contidas na marcha de cada pessoa, essas características são extraídas de sequências de imagens da pessoa caminhando. Os principais desafios dessa técnica biométrica está em extrair as características com informações que consigam diferenciar uma pessoa da outra, além disso, o uso de acessórios e vestimentas dificultam o processo de extração de características. Este trabalho propõe um método de reconhecimento baseado na marcha humana utilizando informações dos contornos das pessoas transformados para o domínio da frequência por meio da Transformada Discreta de Fourier. Como são geradas muitos dados a partir dos contornos, três técnicas diferentes de redução de dimensionalidade CDA (Class Discrimination Ability), PCA (Principal Component Analysis) e PLS (Partial Least Squares) são empregadas para reduzir a quantidade de dados e gerar características que sejam relevantes para o sistema de reconhecimento. Dois classificadores, KNN (K-Nearest Neighbor) e LDA (Linear Discriminant Analysis) classificam as características retornadas pelos métodos de redução de dimensionalidade. As taxas de acurácia são obtidas pelos resultados gerados entre a combinação dos métodos de redução de dimensionalidade e os classificadores, a maior taxa de acurácia foi de 92;67%, a qual foi alcançada com a combinação entre o LDA e PCA (LDAPCA). Dessa forma, conclui-se que as informações contidas no contorno da silhueta no domínio da frequência são discriminantes para reconhecer pessoas através da marcha.

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26

Ganapathi, Tejaswini. "Color Image Based Face Recognition." Thesis, 2008. http://hdl.handle.net/1807/17169.

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Traditional appearance based face recognition (FR) systems use gray scale images, however recently attention has been drawn to the use of color images. Color inputs have a larger dimensionality, which increases the computational cost, and makes the small sample size (SSS) problem in supervised FR systems more challenging. It is therefore important to determine the scenarios in which usage of color information helps the FR system. In this thesis, it was found that inclusion of chromatic information in FR systems is shown to be particularly advantageous in poor illumination conditions. In supervised systems, a color input of optimal dimensionality would improve the FR performance under SSS conditions. A fusion of decisions from individual spectral planes also helps in the SSS scenario. Finally, chromatic information is integrated into a supervised ensemble learner to address pose and illumination variations. This framework significantly boosts FR performance under a range of learning scenarios.

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Liu, Chia-Tsun, and 劉家村. "A New Approach to Biometrics Recognition based on Finger Crease Patterns." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/64361497362504088625.

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碩士
國立中央大學
資訊工程研究所
93
Biometric identification is one of the popular research fields recently. Some of those use the features of hands like fingerprint, hand geometry, palmprint and vein of palm-dorsum. Palmprint has several features, which include principal lines, wrinkles and ridges. Also, the whole stick of finger is full of the similar structure of features (i.e. principal lines, wrinkles and ridges). In this dissertation, a new approach is introduced. In the image of the palm, we try to use Finger Crease Patterns on the central area of four fingers (little finger, ring finger, middle finger and forefinger) as regions of Interest (ROI). Next we computed the wavelet energy feature (WEF) through the use of wavelet transform. Finally, the back propagation neural network (BPNN) is applied for verification.

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28

Fatemian, Seyedeh Zahra. "A Wavelet-based Approach to Electrocardiogram (ECG) and Phonocardiogram (PCG) Subject Recognition." Thesis, 2009. http://hdl.handle.net/1807/18293.

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This thesis studies the applicability of two cardiac traits, the electrocardiogram (ECG) and the phonocardiogram (PCG), as biometrics. There is strong evidence that cardiac electrical activity (ECG) embeds highly distinctive characteristics, suitable for applications such as the recognition of human subjects. On the other hand, having the same origin with the ECG signal, it is believed that the PCG signal conveys distinctive information of an individual which can be deployed in biometric applications. Such recognition systems traditionally provide two modes of functionality, identification and authentication; frameworks for subject recognition are herein proposed and analyzed in both scenarios. Moreover, the expression of the cardiac signals is subject to alternation with heart rate and noise components. Thus, the central consideration of this thesis is the design and evaluation of robust recognition approaches that can compensate for these effects. A recognition system based on each, the ECG and the PCG, is developed and evaluated. Furthermore, a fusion of the two signals in a multimodal biometric system is investigated.

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29

Kong, Adams. "Palmprint Identification Based on Generalization of IrisCode." Thesis, 2007. http://hdl.handle.net/10012/2708.

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The development of accurate and reliable security systems is a matter of wide interest, and in this context biometrics is seen as a highly effective automatic mechanism for personal identification. Among biometric technologies, IrisCode developed by Daugman in 1993 is regarded as a highly accurate approach, being able to support real-time personal identification of large databases. Since 1993, on the top of IrisCode, different coding methods have been proposed for iris and fingerprint identification. In this research, I extend and generalize IrisCode for real-time secure palmprint identification. PalmCode, the first coding method for palmprint identification developed by me in 2002, directly applied IrisCode to extract phase information of palmprints as features. However, I observe that the PalmCodes from the different palms are similar, having many 45o streaks. Such structural similarities in the PalmCodes of different palms would reduce the individuality of PalmCodes and the performance of palmprint identification systems. To reduce the correlation between PalmCodes, in this thesis, I employ multiple elliptical Gabor filters with different orientations to compute different PalmCodes and merge them to produce a single feature, called Fusion Code. Experimental results demonstrate that Fusion Code performs better than PalmCode. Based on the results of Fusion Code, I further identify that the orientation fields of palmprints are powerful features. Consequently, Competitive Code, which uses real parts of six Gabor filters to estimate the orientation fields, is developed. To embed the properties of IrisCode, such as high speed matching, in Competitive Code, a novel coding scheme and a bitwise angular distance are proposed. Experimental results demonstrate that Competitive Code is much more effective than other palmprint algorithms. Although many coding methods have been developed based on IrisCode for iris and palmprint identification, we lack a detailed analysis of IrisCode. One of the aims of this research is to provide such analysis as a way of better understanding IrisCode, extending the coarse phase representation to a precise phase representation, and uncovering the relationship between IrisCode and other coding methods. This analysis demonstrates that IrisCode is a clustering process with four prototypes; the locus of a Gabor function is a two-dimensional ellipse with respect to a phase parameter and the bitwise hamming distance can be regarded as a bitwise angular distance. In this analysis, I also point out that the theoretical evidence of the imposter binomial distribution of IrisCode is incomplete. I use this analysis to develop a precise phase representation which can enhance iris recognition accuracy and to relate IrisCode and other coding methods. By making use of this analysis, principal component analysis and simulated annealing, near optimal filters for palmprint identification are sought. The near optimal filters perform better than Competitive Code in term of d’ index. Identical twins having the closest genetics-based relationship are expected to have maximum similarity in their biometrics. Classifying identical twins is a challenging problem for some automatic biometric systems. Palmprint has been studied for personal identification for many years. However, genetically identical palmprints have not been studied. I systemically examine Competitive Code on genetically identical palmprints for automatic personal identification and to uncover the genetically related palmprint features. The experimental results show that the three principal lines and some portions of weak lines are genetically related features but our palms still contain rich genetically unrelated features for classifying identical twins. As biometric systems are vulnerable to replay, database and brute-force attacks, such potential attacks must be analyzed before they are massively deployed in security systems. I propose projected multinomial distribution for studying the probability of successfully using brute-force attacks to break into a palmprint system based on Competitive Code. The proposed model indicates that it is computationally infeasible to break into the palmprint system using brute-force attacks. In addition to brute-force attacks, I address the other three security issues: template re-issuances, also called cancellable biometrics, replay attacks, and database attacks. A random orientation filter bank (ROFB) is used to generate cancellable Competitive Codes for templates re-issuances. Secret messages are hidden in templates to prevent replay and database attacks. This technique can be regarded as template watermarking. A series of analyses is provided to evaluate the security levels of the measures.

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YAN, MAO-JUN, and 顏卯君. "Pathological Feature-Based ECG Biometric Recognition." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/546mfx.

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碩士
真理大學
資訊工程學系碩士班
105
The biometric recognition technologies focus on the physiological or behavioral characteristics to provide the functionality of human identity verification by adopting pattern recognition algorithms. These technologies are quite advantageous and become an active research field since it is unnecessary to bring any external identity and is not easy to duplicate. In this research, a human identity verification process is provided based on the biometric features of heart activities. In this study, we exploit the medical definition of heart disease characteristics, such as atrial hypertrophy, sinus rhythm, atrioventricular block and other symptoms, as our characterization of identity called pathological features. Our proposal incorporates these pathological characteristics of heart activities to define a set of features from ECG data. This feature set is then cross validated by several classifier models to observe the performance of the features. In our control and experimental group experiments, it is found that the recognition process based on the classification model created from pathological features is actually advantageous than traditional methods. The well-known rhythm database, MIT-BIH arrhythmia database, MIT-BIH normal sinus rhythm database and QT database are used to obtain the basic point of each cardiac cycle (PQRST), as the pathological features to take the benchmark. The result shows that the proposed method reached a higher verification accuracy than the traditional wavelet method. This result actually presents the excellence of the pathological features for biometric recognition. This paper also demonstrated an experimental ECG identification system based on ECG signal sensor AD8232 and embedded microcomputer Banana Pi to prove the feasibility of our biometric recognition method.

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Chih, Yu-Ting, and 池御婷. "Machine Learning-Based Electrocardiography (ECG) Biometric Recognition." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/81765430593108810469.

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Deng, Peter Shaohua, and 鄧少華. "Biometric-based Pattern Recognition -- Handwritten Signature Verification and Face Recognition." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/79526579531096548003.

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博士
國立中央大學
資訊工程研究所
88
In this dissertation, two biometric-based pattern recognition problems were studied, i.e., off-line handwritten signature verification and human face recognition. Biometrics, by definition, is the automated technique of measuring a physical characteristic or person trait of an individual and comparing the characteristic or trait to a database for purposes of recognizing or authenticating that individual. Biometrics uses physical characteristics, defined as the things we are, and personal traits, defined as the things we behave, including facial thermographs, chemical composition of body odor, retina and iris, fingerprints, hand geometry, skin pores, wrist/hand veins, handwritten signature, keystrokes or typing, and voiceprint. To deal with the first biometric-based pattern recognition problem, i.e., off-line handwritten signature verification. Wavelet theory, zero-crossing, dynamic time warping, and nonlinear integer programming form the main body of our methodology. The proposed system can automatically identify useful features which consistently exist within different signatures of the same person and, based on these features, verify whether a signature is a forgery or not. The system starts with a closed-contour tracing algorithm. The curvature data of the traced closed contours are decomposed into multiresolutional signals using wavelet transforms. Then the zero-crossings corresponding to the curvature data are extracted as features for matching. Moreover, a statistical measurement is devised to decide systematically which closed contours and their associated frequency data of a writer are most stable of a writer are most stable and discriminating. Based on these data, the optimal threshold value which controls the accuracy of the feature extraction process is calculated. The proposed approach can be applied to both on-line and off-line signature verification systems. The second biometric-based pattern recognition problem we deal with is human face recognition; we applied the minimum classification error (MCE) technique proposed by Juang and Katagiri[11]. In this technique, the classical discriminant analysis methodology is blended with the classification rule in a new functional form and is used as the design objective criterion to be optimized by numerical search algorithm. In our work, the MCE formulation is incorporated into a three-layer neural network classifier called multilayer perceptron (MLP). Unlike the traditional probabilistic-based Bayes decision technique, the proposed approach is not necessary to assume the probability model of each class. Besides, the classifier works well even when the size of a training set is small. Moreover, no matter in normal environment or harsh environment, the MCE-based method is superior to the minimum sum-squared error (MSE) based method which is commonly used in traditional neural network classifier. Finally, by incorporating a fast face detection algorithm into the system to help for extracting the face-only image from a complex background, the MCE-based face recognition system is robust to image acquired from harsh environment. Experimental results confirm that our approach outperforms the previous approaches.

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33

Serrano, Luís Miguel dos Santos. "Hand-based biometric recognition system for mobile devices." Master's thesis, 2011. http://hdl.handle.net/10071/8235.

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34

Santos, Gil Melfe Mateus. "Biometric recognition in unconstrained environments." Doctoral thesis, 2015. http://hdl.handle.net/10400.6/4041.

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Every human being is entitled, by his very nature, to a set of physiological and behavioral features that characterize him. The study of such features led to the development of a considerable amount of systems and applications, referred as biometric systems. The use of biometric systems has been significantly growing over the last years, particularly in the field of security: authentication, access control, criminal identification, etc. Being a highly demanding sector, it is then natural that greater focus is placed on the biometric traits that are able to deliver high discrimination between subjects whilst being less prone to forgery. However, such constraints represent a significant impact on both system’s usability and flexibility, requiring from the user a significant amount of cooperation. In this context, the iris is a primordial trait. Existing biometric recognition systems based on the iris follow the pioneer approach proposed by John Daugman, that proved itself as an excellent option for cooperative scenarios where images are acquired in the near-infrared spectrum. However, not in every case user cooperation is expected and, when not, systems with such high acquisition constraints are of little or no use. Research is then focused on circumventing those issues, either by improving the existing methods or finding new and more fitting traits. On the later, the periocular region (i.e., the region surrounding the eye) is one of the most promising characteristics: it mimics a natural and spontaneous way of recognition employed by the human beings; has an advantageous localization in relation to the iris, making it easy to be simultaneously acquired; and has, as corroborated by the literature, a set of promising characteristics that can be used for recognition purposes. The main objective of this doctoral work is then to either adapt or develop a novel biometric recognition system, suited for in the wild environments. Such systems should preferably use the periocular region as biometric trait, due to its flexibility and ease of acquisition in adverse conditions, and keep the operation constraints as low as possible. Subjects can be imaged ata- distance, on-the-move, and under irregular lighting conditions, using cameras working in the visible wavelength. To accomplish such goal, a set of intermediate milestones was established. At first, the iris was studied as biometric trait, paying particular attention to the techniques for allowing its use on in the wild scenarios. The effects of the visible wavelength light on iris performance for biometric purposes should not be disregarded and, as so, this factor was also studied. After rolling out iris appropriateness as the main distinctive feature, different emerging traits were analyzed, with special attention being paid to the periocular region. The most relevant methods were implemented and tested against the same dataset. Ultimately, multiple contributions were proposed and accepted by the scientific community, with applicability on different in the wild environments, the last of which is the proposal of an actual biometric system, working in real challenging conditions.
Ao ser humano está associado, pela sua natureza, um conjunto de características físicas e comportamentais que o caracterizam. O estudo dessas características permitiu o desenvolvimento de um considerável número de sistemas e aplicações – sistemas biométricos. A utilização de sistemas biométricos tem vindo a aumentar ao longo dos últimos anos, principalmente na área da segurança: autenticação, controlo de acesso, identificação criminal, etc. Sendo um sector de elevada exigência, é natural que se dê maior destaque às características biométricas que permitam atingir uma elevada distinção entre os sujeitos, sendo pouco propensas a falsificação. Contudo, estas restrições acarretam um impacto significativo quer na usabilidade do sistema quer na sua flexibilidade, sendo necessário de um elevado grau de cooperação por parte do utilizador. É neste contexto que a íris é apresentada como a característica biométrica por excelência. Os sistemas de reconhecimento biométrico que utilizam a íris como característica principal baseiam-se essencialmente na abordagem pioneira proposta por John Daugman. Esta abordagem demonstrou ser uma excelente opção para cenários cooperativos de reconhecimento em que as imagens possam ser adquiridas no infravermelho. Contudo, nem sempre a cooperação por parte dos indivíduos é expectável. Nesses casos, sistemas com elevadas restrições na aquisição deixam de ser viáveis. Linhas de investigação mais recentes tentam contornar este problema, seguindo duas possíveis abordagens: adaptação dos métodos existentes aos novos cenários e desafios; e procura de novas características biométricas que melhor se adaptem a esta realidade. É nesta última abordagem que a região periocular (i.e., o olho e a região circundante) se assume como uma das características mais promissoras: aproxima-se do método de reconhecimento usado naturalmente e de forma espontânea pelo ser humano; tem uma localização privilegiada em relação à íris, facilitando a aquisição simultânea de ambos os sinais biométricos; e tem, tal como corroborado pela literatura, um conjunto de características promissoras, passíveis de ser usadas para efeitos de reconhecimento. O objectivo principal destes trabalhos de doutoramento é então desenvolver (ou adaptar) um sistema de reconhecimento biométrico, especialmente adequado para ambientes não-controlados (i.e., in the wild). Tal sistema, pelos seus requisitos e especificidades, deverá usar como característica preferencial de reconhecimento a região periocular, dado que esta permite uma maior flexibilização e facilidade de aquisição em condições particularmente adversas, minimizando assim as restrições de funcionamento. Os indivíduos poderão ser reconhecidos a distâncias superiores, em movimento, com condições de iluminação irregulares, e usando informação adquirida no espectro de luz visível. Por forma a atingir este objectivo, uma série de etapas intermédias foi estabelecida. Começou por se estudar a íris enquanto sinal biométrico, prestando especial atenção à vertente nãocooperativa e ao funcionamento no comprimento de onda visível. Este estudo englobou também os efeitos da luz visível no processo de reconhecimento, tendo sido levada a cabo uma análise da reflectância da íris em função do comprimento de onda de diferentes iluminantes. Tendo sido observado que a íris não se apresenta como sinal ideal ao reconhecimento in the wild, foram estudadas características biométricas emergentes, prestando especial atenção à região periocular. Da literatura analisada, os métodos mais relevantes foram implementados e testados contra um mesmo conjunto de dados. Finalmente, várias contribuições foram propostas e aceites pela comunidade científica, com aplicação em diferentes ambientes não-controlados, tendo sido a última a conceptualização de um sistema biométrico capaz de trabalhar nas condições desafiantes a que nos propúnhamos.

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35

Martins, Diogo Santos. "Biometric recognition based on the texture along palmprint lines." Master's thesis, 2011. http://hdl.handle.net/10216/61287.

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36

Nickel, Claudia. "Accelerometer-based Biometric Gait Recognition for Authentication on Smartphones." Phd thesis, 2012. https://tuprints.ulb.tu-darmstadt.de/3014/4/20120620_Dissertation_Nickel_final.pdf.

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The authentication via accelerometer-based biometric gait recognition offers a user-friendly alternative to common authentication methods on smartphones. It has the great advantage that the authentication can be performed without user interaction. When the user is walking, his walk-pattern can be extracted from the accelerations measured using the integrated sensors of the smartphone. This pattern can be used for authentication. A study showed that users often deactivate the authentication methods of their mobile devices because they consume too much time. Because all steps necessary to perform biometric gait recognition can be executed in the background, no user interaction is necessary for the presented technique. Performing a continuous authentication while the user is walking, an up-to-date authentication result is available at any point in time. During log-in, no calculations are necessary anymore, hence there is no delay. Only in cases where the user is not walking, an alternative authentication method has to be used. This is a great benefit for the user because he has the advantages of a phone which is protected by authentication but without the disadvantages common methods impose. This high user-friendliness is likely to increase the number of smartphones for which the screen-lock is linked with an authentication. Therefore, a higher security of the data stored in smartphones can be achieved. A misuse of the stored information by an unauthorized user can be prevented. Due to the growing distribution of powerful smartphones, the number of available applications is increasing as well. These applications result in a growing amount of data stored on the devices, which make the protection of the device necessary. These data comprise e.g. addresses, appointments or GPS-information. Additionally, some applications, e.g. of e-mail-providers or social networks, require the user to authenticate himself. Often these credentials are stored by the user on the phone, such that it is not necessary to enter them each time. In case an unauthorized person has access to such a phone he can use these services without restrictions and therefore substantially harm the user. The objective of this thesis was to develop methods for accelerometer-based biometric gait recognition which achieve sufficient low error rates, as well as to demonstrate that their computational effort is low and allows for an execution on current smartphones. Because the basis of existing methods is the extraction of gait cycles (i.e. two steps) from the accelerometer data, a cycle-based method was developed and evaluated in a scenario test. This method uses raw data of the gait cycles as feature vectors and accomplishes the classification using distance functions. In addition, a further approach was selected, which does not need the time-costly and error-prone gait cycle extraction. Instead, it is using overlapping segments of a fixed time length. Several features are extracted from these segments and combined to feature vectors. Machine learning algorithms are used for classification. A benchmark of the approaches on a challenging database showed that these methods yield low equal error rates between 6% and 7% and are outperforming the cycle-based methods. These error rates were achieved under the realistic conditions that training and probe data are not collected on the same day. It was shown that five minutes of gait data are sufficient to thoroughly train the models. It should be regarded that the training data contain the different walking velocities at which the user should be recognized later on. To obtain low false rejection rates, the classification should be based on around three minutes walk data. Two of the developed methods were implemented on a smartphone. It was shown that both methods are able to perform the classification fast enough to allow for an authentication without delay for the user.

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37

Martins, Diogo Santos. "Biometric recognition based on the texture along palmprint lines." Dissertação, 2011. http://hdl.handle.net/10216/61287.

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38

Al-Waisy, Alaa S., Rami S. R. Qahwaji, Stanley S. Ipson, Shumoos Al-Fahdawi, and Tarek A. M. Nagem. "A multi-biometric iris recognition system based on a deep learning approach." 2017. http://hdl.handle.net/10454/15682.

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Yes
Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person.

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39

Wu, Jia-Yang, and 吳家揚. "Non-Invasive Blood Glucose Estimation and Biometric Recognition Method Based on Photoplethysmography Signal." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/mbermw.

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40

Chiang, Yao-Shan, and 江樂山. "Palm-Print and Hand-Shape Biometric Recognition System Based on Wavelet Transform and Statistical Moments." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/65358393795776210022.

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碩士
國立暨南國際大學
電機工程學系
93
With an increasing emphasis on security, personal authentication based on biometrics has been receiving extensive attention over the past decade. Among many different biometric technologies, this thesis examines palm-print and hand-shape technique for personal identification and develops a good performance recognition system based on human hand features. It is implemented and tested on VIP-CC Lab. hand image database. The proposed system includes four modules: image acquisition, image pre-processing, feature extraction, and recognition modules. First, the system captures a hand image using digital camera, then uses some image processing algorithms to localize the region of the interest of palm-print and hand-geometry from the hand image via image pre-processing module. The feature extraction module adopts the gradient direction （i.e., angle） of the two different wavelet transforms in the palm-print phase, and adopts the statistical moments in the hand-shape to extract the discriminating texture features. The system encodes the feature to generate its palm-print codes by binary gray coding, and uses invariant moment vector in hand-geometry phase. Finally, the system applies these feature codes and vector for matching in recognition module. Experimental results show that the system has an encouraging performance on the VIP-CC Lab. database（including 210 images from 30 classes）. The proposed system adopts two different wavelet transform and statistical moments to extract palm-print and hand-shape features, then uses the gradient direction coding to generate the feature codes. We attain the recognition rates up to 95.00% and 98.33%（according to equal error rate, EER）, respectively. Even under the circumstance of false acceptance rate（FAR） 0%, the system still approaches the recognition rate above 89.17%（acceptance of authentic, AA）. This thesis analyzes the experimented results and verifies the related inferences of the proposed system for providing useful information for further research.

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41

Moolla, Yaseen. "Handwritten signature verification using locally optimized distance-based classification." Thesis, 2012. http://hdl.handle.net/10413/10112.

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Although handwritten signature verification has been extensively researched, it has not achieved optimum accuracy rate. Therefore, efficient and accurate signature verification techniques are required since signatures are still widely used as a means of personal verification. This research work presents efficient distance-based classification techniques as an alternative to supervised learning classification techniques (SLTs). Two different feature extraction techniques were used, namely the Enhanced Modified Direction Feature (EMDF) and the Local Directional Pattern feature (LDP). These were used to analyze the effect of using several different distance-based classification techniques. Among the classification techniques used, are the cosine similarity measure, Mahalanobis, Canberra, Manhattan, Euclidean, weighted Euclidean and fractional distances. Additionally, the novel weighted fractional distances, as well as locally optimized resampling of feature vector sizes were tested. The best accuracy was achieved through applying a combination of the weighted fractional distances and locally optimized resampling classification techniques to the Local Directional Pattern feature extraction. This combination of multiple distance-based classification techniques achieved accuracy rate of 89.2% when using the EMDF feature extraction technique, and 90.8% when using the LDP feature extraction technique. These results are comparable to those in literature, where the same feature extraction techniques were classified with SLTs. The best of the distance-based classification techniques were found to produce greater accuracy than the SLTs.
Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2012.

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42

"The statistical evaluation of minutiae-based automatic fingerprint verification systems." Thesis, 2006. http://library.cuhk.edu.hk/record=b6074180.

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Basic technologies for fingerprint feature extraction and matching have been improved to such a stage that they can be embedded into commercial Automatic Fingerprint Verification Systems (AFVSs). However, the reliability of AFVSs has kept attracting concerns from the society since AFVSs do fail occasionally due to difficulties like problematic fingers, changing environments, and malicious attacks. Furthermore, the absence of a solid theoretical foundation for evaluating AFVSs prevents these failures from been predicted and evaluated. Under the traditional empirical AFVS evaluation framework, repeated verification experiments, which can be very time consuming, have to be performed to test whether an update to an AFVS can really lead to an upgrade in its performance. Also, empirical verification results are often unable to provide deeper understanding of AFVSs. To solve these problems, we propose a novel statistical evaluation model for minutiae-based AFVSs based on the understanding of fingerprint minutiae patterns. This model can predict the verification performance metrics as well as their confidence intervals. The analytical power of our evaluation model, which makes it superior to empirical evaluation methods, can assist system developers to upgrade their AFVSs purposefully. Also, our model can facilitate the theoretical analysis of the advantages and disadvantages of various fingerprint verification techniques. We verify our claims through different and extensive experiments.
Chen, Jiansheng.
"November 2006."
Adviser: Yiu-Sang Moon.
Source: Dissertation Abstracts International, Volume: 68-08, Section: B, page: 5343.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2006.
Includes bibliographical references (p. 110-122).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstracts in English and Chinese.
School code: 1307.

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Bhat, Srikrishna K. K. "A Study Of Utility Of Smile Profile For Face Recognition." Thesis, 2006. http://hdl.handle.net/2005/359.

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Face recognition is one of the most natural activities performed by the human beings. It has wide range of applications in the areas of Human Computer Interaction, Surveillance, Security etc. Face information of people can be obtained in a non-intrusive manner, without violating privacy. But, robust face recognition which is invariant under varying pose, illumination etc is still a challenging problem. The main aim of this thesis is to explore the usefulness of smile proﬁle of human beings as an extra aid in recognizing people by faces. Smile proﬁle of a person is the sequence of images captured by a camera when the person voluntarily smiles. Using sequence of images instead of a single image will increase the required computational resources signiﬁcantly. The challenge here is to design a feature extraction technique from a smile sample, which is useful for authentication and is also efﬁcient in terms of storage and computational aspects. There are some experimental evidences which support the claim that facial expressions have some person speciﬁc information. But, to the best of our knowledge, systematic study of a particular facial expression for biometrical purposes has not been done so far. The smile proﬁle of human beings, which is captured under some reasonably controlled setup, is used for ﬁrst time for face recognition purpose. As a ﬁrst step, we applied two of the recent subspace based face classiﬁers on the smile samples. We were not able to obtain any conclusive results out of this experiment. Next we extracted features using only the difference vectors obtained from smile samples. The difference vectors depend only on the variations which occur in the corresponding smile proﬁle. Hence any characterization we obtain from such features can be fully attributed to the smiling action. The feature extraction technique we employed is very much similar to PCA. The smile signature that we have obtained is named as Principal Direction of Change(PDC). PDC is a unit vector (in some high dimensional space) which represents the direction in which the major changes occurred during the smile. We obtained a reasonable recognition rate by applying Nearest Neighbor Classifier(NNC) on these features. In addition to that, these features turn out to be less sensitive to the speed of smiling action and minor variations in face detection and head orientation, while capturing the pattern of variations in various regions of face due to smiling action. Using set of experiments on PDC based features we establish that smile has some person specific characteristics. But the recognition rates of PDC based features are less than the recent conventional techniques. Next we have used PDC based features to aid a conventional face classifier. We have used smile signatures to reject some candidate faces. Our experiments show that, using smile signatures, we can reject some of the potential false candidate faces which would have been accepted by the conventional face classifier. Using this smile signature based rejection, the performance of the conventional classifier is improved significantly. This improvement suggests that, the biometric information available in smile profiles does not exist in still images. Hence the usefulness of smile profiles for biometric applications is established through this experimental investigation.

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