Relevant bibliographies by topics / Biometrics based recognition

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Academic literature on the topic 'Biometrics based recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 January 2023

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Journal articles on the topic "Biometrics based recognition"

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OMOTOSHO, LAWRENCE, IBRAHIM OGUNDOYIN, OLAJIDE ADEBAYO, and JOSHUA OYENIYI. "AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK." Journal of Engineering Studies and Research 27, no. 2 (October 10, 2021): 73–81. http://dx.doi.org/10.29081/jesr.v27i2.276.

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Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. In this paper, we developed a face-iris multimodal biometric recognition system based on convolutional neural network for feature extraction, fusion at feature level, training and matching to reduce dimensionality, error rate and improve the recognition accuracy suitable for an access control. Convolutional Neural Network is based on deep supervised learning model and was employed for training, classification, and testing of the system. The images are preprocessed to a standard normalization and then flow into couples of convolutional layers. The developed multimodal biometrics system was evaluated on a dataset of 700 iris and facial images, the training database contain 600 iris and face images, 100 iris and face images were used for testing. Experimental result shows that at the learning rate of 0.0001, the multimodal system has a performance recognition accuracy (RA) of 98.33% and equal error rate (ERR) of 0.0006%.
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Ramana N., Venkata, S. Anu H. Nair, and K. P. Sanal Kumar. "Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 1s (December 9, 2022): 99–107. http://dx.doi.org/10.17762/ijritcc.v10i1s.5798.

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Biometric security has become a main concern in the data security field. Over the years, initiatives in the biometrics field had an increasing growth rate. The multimodal biometric method with greater recognition and precision rate for smart cities remains to be a challenge. By comparison, made with the single biometric recognition, we considered the multimodal biometric recognition related to finger vein and fingerprint since it has high security, accurate recognition, and convenient sample collection. This article presents a Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification (MFFODL-MBV) model. The presented MFFODL-MBV technique performs biometric verification using multiple biometrics such as fingerprint, DNA, and microarray. In the presented MFFODL-MBV technique, EfficientNet model is employed for feature extraction. For biometric recognition, MFFO algorithm with long short-term memory (LSTM) model is applied with MFFO algorithm as hyperparameter optimizer. To ensure the improved outcomes of the MFFODL-MBV approach, a widespread experimental analysis was performed. The wide-ranging experimental analysis reported improvements in the MFFODL-MBV technique over other models.
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Haider, Syed Aqeel, Yawar Rehman, and S. M. Usman Ali. "Enhanced Multimodal Biometric Recognition Based upon Intrinsic Hand Biometrics." Electronics 9, no. 11 (November 14, 2020): 1916. http://dx.doi.org/10.3390/electronics9111916.

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In the proposed study, we examined a multimodal biometric system having the utmost capability against spoof attacks. An enhanced anti-spoof capability is successfully demonstrated by choosing hand-related intrinsic modalities. In the proposed system, pulse response, hand geometry, and finger–vein biometrics are the three modalities of focus. The three modalities are combined using a fuzzy rule-based system that provides an accuracy of 92% on near-infrared (NIR) images. Besides that, we propose a new NIR hand images dataset containing a total of 111,000 images. In this research, hand geometry is treated as an intrinsic biometric modality by employing near-infrared imaging for human hands to locate the interphalangeal joints of human fingers. The L2 norm is calculated using the centroid of four pixel clusters obtained from the finger joint locations. This method produced an accuracy of 86% on the new NIR image dataset. We also propose finger–vein biometric identification using convolutional neural networks (CNNs). The CNN provided 90% accuracy on the new NIR image dataset. Moreover, we propose a robust system known as the pulse response biometric against spoof attacks involving fake or artificial human hands. The pulse response system identifies a live human body by applying a specific frequency pulse on the human hand. About 99% of the frequency response samples obtained from the human and non-human subjects were correctly classified by the pulse response biometric. Finally, we propose to combine all three modalities using the fuzzy inference system on the confidence score level, yielding 92% accuracy on the new near-infrared hand images dataset.
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Oh, Junhyoung, Ukjin Lee, and Kyungho Lee. "Usability Evaluation Model for Biometric System considering Privacy Concern Based on MCDM Model." Security and Communication Networks 2019 (March 27, 2019): 1–14. http://dx.doi.org/10.1155/2019/8715264.

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Biometric devices play an integral role in consumer’s daily life, providing a seamless environment. However, it is essential to measure the usability of biometrics, owing to the elements of biometrics satisfying both usability and security. This study redefines the elements of biometrics pertaining to usability determined in previous studies and adds elements of psychological relevance, such as privacy concerns. To organize the interrelated usability structure systemically, this paper applies the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) to derive the usability structure. Thereupon, the established structure is applied in the clustered weighted Analytical Network Processes (ANP) to generate the proposed usability evaluation model. By these methods, the pertinent relationships between the factors are clarified and the weight of each element is determined. In the empirical study, 106 students measured usability of the fingerprint recognition system, iris recognition system, and facial recognition system employing our usability evaluation model. The results of this model generate the quantitative score of usability for biometric systems and suggest strategies to increase the score. The proposed usability evaluation model can comprehensively assist usability practitioners to evaluate biometric systems.
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Drosou, A., D. Ioannidis, K. Moustakas, and D. Tzovaras. "Unobtrusive Behavioral and Activity-Related Multimodal Biometrics: The ACTIBIO Authentication Concept." Scientific World JOURNAL 11 (2011): 503–19. http://dx.doi.org/10.1100/tsw.2011.51.

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Unobtrusive Authentication Using ACTIvity-Related and Soft BIOmetrics (ACTIBIO) is an EU Specific Targeted Research Project (STREP) where new types of biometrics are combined with state-of-the-art unobtrusive technologies in order to enhance security in a wide spectrum of applications. The project aims to develop a modular, robust, multimodal biometrics security authentication and monitoring system, which uses a biodynamic physiological profile, unique for each individual, and advancements of the state of the art in unobtrusive behavioral and other biometrics, such as face, gait recognition, and seat-based anthropometrics. Several shortcomings of existing biometric recognition systems are addressed within this project, which have helped in improving existing sensors, in developing new algorithms, and in designing applications, towards creating new, unobtrusive, biometric authentication procedures in security-sensitive, Ambient Intelligence environments. This paper presents the concept of the ACTIBIO project and describes its unobtrusive authentication demonstrator in a real scenario by focusing on the vision-based biometric recognition modalities.
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Pontoh, Fransisca J., Jayanti Yusmah Sari, Amil A. Ilham, and Ingrid Nurtanio. "MULTISPECTRAL DORSAL HAND VEIN RECOGNITION BASED ON LOCAL LINE BINARY PATTERN." Jurnal Ilmu Komputer dan Informasi 11, no. 2 (June 29, 2018): 95. http://dx.doi.org/10.21609/jiki.v11i2.576.

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Nowadays, dorsal hand vein recognition is one of the most recent multispectral biometrics technologies used for the person identification/authentication. Looking into another biometrics system, dorsal hand vein biometrics system has been popular because of the privilege: false duplicity, hygienic, static, and convenient. The most challenging phase in a biometric system is feature extraction phase. In this research, feature extraction method called Local Line Binary Pattern (LLBP) has been explored and implemented. We have used this method to our 300 dorsal hand vein images obtained from 50 persons using a low-cost infrared webcam. In recognition step, the adaptation fuzzy k-NN classifier is to evaluate the efficiency of the proposed approach is feasible and effective for dorsal hand vein recognition. The experimental result showed that LLBP method is reliable for feature extraction on dorsal hand vein recognition with a recognition accuracy up to 98%.
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Karmakar, Dhiman, Madhura Datta, and C. A. Murthy. "Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique." International Journal of Software Science and Computational Intelligence 5, no. 3 (July 2013): 22–32. http://dx.doi.org/10.4018/ijssci.2013070102.

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Biometric recognition techniques attracted the researchers for the last two decades due to their many applications in the field of security. In recent times multimodal biometrics have been found to perform better, in several aspects, over unimodal biometrics. The classical approach for recognition is based on dissimilarity measure and for the sake of proper classification one needs to put a threshold on the dissimilarity value. In this paper an intra-class threshold for multimodal biometric recognition procedure has been developed. The authors' selection method of threshold is based on statistical set estimation technique which is applied on a minimal spanning tree and consisting of fused face and iris images. The fusion is performed here on feature level using face and iris biometrics. The proposed method, applied on several multimodal datasets, found to perform better than traditional ROC curve based threshold technique.
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D S, Dr Dinesh Kumar. "Human Authentication using Face, Voice and Fingerprint Biometrics." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 853–62. http://dx.doi.org/10.22214/ijraset.2021.36381.

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Multimodal biometric approaches are growing in importance for personal verification and identification, since they provide better recognition results and hence improve security compared to biometrics based on a single modality. In this project, we present a multimodal biometric system that is based on the fusion of face, voice and fingerprint biometrics. For face recognition, we employ Haar Cascade Algorithm, while minutiae extraction is used for fingerprint recognition and we will be having a stored code word for the voice authentication, if any of these two authentication becomes true, the system consider the person as authorized person. Fusion at matching score level is then applied to enhance recognition performance. In particular, we employ the product rule in our investigation. The final identification is then performed using a nearest neighbour classifier which is fast and effective. Experimental results confirm that our approach achieves excellent recognition performance, and that the fusion approach outperforms biometric identification based on single modalities.
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Jain, Anil K., and Arun Ross. "Bridging the gap: from biometrics to forensics." Philosophical Transactions of the Royal Society B: Biological Sciences 370, no. 1674 (August 5, 2015): 20140254. http://dx.doi.org/10.1098/rstb.2014.0254.

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Biometric recognition, or simply biometrics, refers to automated recognition of individuals based on their behavioural and biological characteristics. The success of fingerprints in forensic science and law enforcement applications, coupled with growing concerns related to border control, financial fraud and cyber security, has generated a huge interest in using fingerprints, as well as other biological traits, for automated person recognition. It is, therefore, not surprising to see biometrics permeating various segments of our society. Applications include smartphone security, mobile payment, border crossing, national civil registry and access to restricted facilities. Despite these successful deployments in various fields, there are several existing challenges and new opportunities for person recognition using biometrics. In particular, when biometric data is acquired in an unconstrained environment or if the subject is uncooperative, the quality of the ensuing biometric data may not be amenable for automated person recognition. This is particularly true in crime-scene investigations, where the biological evidence gleaned from a scene may be of poor quality. In this article, we first discuss how biometrics evolved from forensic science and how its focus is shifting back to its origin in order to address some challenging problems. Next, we enumerate the similarities and differences between biometrics and forensics. We then present some applications where the principles of biometrics are being successfully leveraged into forensics in order to solve critical problems in the law enforcement domain. Finally, we discuss new collaborative opportunities for researchers in biometrics and forensics, in order to address hitherto unsolved problems that can benefit society at large.
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Arunachalamand, MuthuKumar, and Kavipriya Amuthan. "Finger Knuckle Print Recognition using MMDA with Fuzzy Vault." International Arab Journal of Information Technology 17, no. 4 (July 1, 2020): 554–61. http://dx.doi.org/10.34028/iajit/17/4/14.

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Currently frequent biometric scientific research such as with biometric applications like face, iris, voice, hand-based biometrics traits like palm print and fingerprint technique are utilized for spotting out the persons. These specific biometrics habits have their own improvement and weakness so that no particular biometrics can adequately opt for all terms like the accuracy and cost of all applications. In recent times, in addition, to distinct with the hand-based biometrics technique, Finger Knuckle Print (FKP) has been appealed to boom the attention among biometric researchers. The image template pattern formation of FKP embraces the report that is suitable for spotting the uniqueness of individuality. This FKP trait observes a person based on the knuckle print and the framework in the outer finger surface. This FKP feature determines the line anatomy and finger structures which are well established and persistent throughout the life of an individual. In this paper, a novel method for personal identification will be introduced, along with that data to be stored in a secure way has also been proposed. The authentication process includes the transformation of features using 2D Log Gabor filter and Eigen value representation of Multi-Manifold Discriminant Analysis (MMDA) of FKP. Finally, these features are grouped using k-means clustering for both identification and verification process. This proposed system is initialized based on the FKP framework without a template based on the fuzzy vault. The key idea of fuzzy vault storing is utilized to safeguard the secret key in the existence of random numbers as chaff pints
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Dissertations / Theses on the topic "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.
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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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Books on the topic "Biometrics based recognition"

1

Melin, Patricia, Janusz Kacprzyk, and Witold Pedrycz, eds. Soft Computing for Recognition Based on Biometrics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15111-8.

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L, Wilson C. Simple test procedure for image-based biometric verification systems. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Hayit, Greenspan, Syeda-Mahmood Tanveer, and SpringerLink (Online service), eds. Medical Content-Based Retrieval for Clinical Decision Support: Second MICCAI International Workshop, MCBR-CDS 2011, Toronto, ON, Canada, September 22, 2011, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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1946-, Kittler Josef, and Nixon Mark S, eds. Audio-and video-based biometric person authentication: 4th International Conference, AVBPA 2003, Guildford, UK, June 2003 : proceedings. Berlin: Springer, 2003.

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AVBPA '97 ((1st 1997 Montana,Switzerland). Audio- and video-based biometric person authentication: First International Conference, AVBPA '97, Crans-Montana, Switzerland, March 1997 : proceedings. Berlin: Springer, 1997.

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International Conference, AVBPA (1st 1997 Montana, Switzerland). Audio- and video-based biometric person authentication: First International Conference, AVBPA '97, Crans-Montana, Switzerland, March 12-14, 1997 : proceedings. Berlin: Springer, 1997.

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Driss, Mammass, Lézoray Olivier, Nouboud Fathallah, Aboutajdine Driss, and SpringerLink (Online service), eds. Image and Signal Processing: 5th International Conference, ICISP 2012, Agadir, Morocco, June 28-30, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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MCBR-CDS 2009 (2009 London, England). Medical content-based retrieval for clinical decision support: First MICCAI international workshop, MCBR-CDS 2009, London, UK, September 20, 2009 : revised selected papers. Berlin: Springer, 2010.

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Soft Computing For Recognition Based On Biometrics. Springer, 2010.

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Pedrycz, Witold, and Patricia Melin. Soft Computing for Recognition based on Biometrics. Springer, 2011.

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Book chapters on the topic "Biometrics based recognition"

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Syed, Zahid, Sean Banerjee, and Bojan Cukic. "Pointer-Based Recognition." In Encyclopedia of Biometrics, 1–9. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27733-7_9211-2.

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Syed, Zahid, Sean Banerjee, and Bojan Cukic. "Pointer-Based Recognition." In Encyclopedia of Biometrics, 1261–68. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7488-4_9211.

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Štepec, Dejan, Žiga Emeršič, Peter Peer, and Vitomir Štruc. "Constellation-Based Deep Ear Recognition." In Deep Biometrics, 161–90. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-32583-1_8.

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Yam, Chew-Yean, and Mark Nixon. "Gait Recognition, Model-Based." In Encyclopedia of Biometrics, 1–8. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27733-7_37-3.

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Xie, Xiaohui, Fei Su, and Anni Cai. "Ridge-Based Fingerprint Recognition." In Advances in Biometrics, 273–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11608288_37.

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Boyd, Jeffrey E., and James J. Little. "Gait Recognition, Silhouette-Based." In Encyclopedia of Biometrics, 646–52. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_36.

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Yam, Chew-Yean, and Mark S. Nixon. "Gait Recognition, Model-Based." In Encyclopedia of Biometrics, 633–39. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_37.

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Hamsici, Onur C., and Aleix M. Martinez. "Face Recognition, Component-Based." In Encyclopedia of Biometrics, 338–47. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_93.

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Chellappa, Rama, Gaurav Aggarwal, and S. Kevin Zhou. "Face Recognition, Video-Based." In Encyclopedia of Biometrics, 366–72. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_96.

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Kakadiaris, Ioannis A., Georgios Passalis, George Toderici, Takis Perakis, and Theoharis Theoharis. "Face Recognition, 3D-Based." In Encyclopedia of Biometrics, 329–38. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_97.

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Conference papers on the topic "Biometrics based recognition"

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De A. S. M., Juliana, and Márjory Da Costa-Abreu. "An evaluation of a three-modal hand-based database to forensic-based gender recognition." In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação, 2019. http://dx.doi.org/10.5753/sbseg.2019.13989.

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In recent years, behavioural soft-biometrics have been widely used to improve biometric systems performance. Information like gender, age and ethnicity can be obtained from more than one behavioural modality. In this paper, we propose a multimodal hand-based behavioural database for gender recognition. Thus, our goal in this paper is to evaluate the performance of the multimodal database. For this, the experiment was realised with 76 users and was collected keyboard dynamics, touchscreen dynamics and handwritten signature data. Our approach consists of compare two-modal and one-modal modalities of the biometric data with the multimodal database. Traditional and new classifiers were used and the statistical Kruskal-Wallis to analyse the accuracy of the databases. The results showed that the multimodal database outperforms the other databases.
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Jeong, MinYi, Chulhan Lee, Jongsun Kim, Jeung-Yoon Choi, Kar-Ann Toh, and Jaihie Kim. "Changeable Biometrics for Appearance Based Face Recognition." In 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference. IEEE, 2006. http://dx.doi.org/10.1109/bcc.2006.4341629.

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Doublet, J., O. Lepetit, and M. Revenu. "Contactless Hand Recognition Based on Distribution Estimation." In 2007 Biometrics Symposium. IEEE, 2007. http://dx.doi.org/10.1109/bcc.2007.4430547.

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Amin, Tahir, and Dimitrios Hatzinakos. "A Correlation Based Approach to Human Gait Recognition." In 2007 Biometrics Symposium. IEEE, 2007. http://dx.doi.org/10.1109/bcc.2007.4430550.

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Jangua, Daniel, and Aparecido Marana. "A New Method for Gait Recognition Using 2D Poses." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wvc.2020.13483.

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Over the last decades, biometrics has become an important way for human identification in many areas, since it can avoid frauds and increase the security of individuals in society. Nowadays, most popular biometric systems are based on fingerprint and face features. Despite the great development observed in Biometrics, an important challenge lasts, which is the automatic people identification in low-resolution videos captured in unconstrained scenarios, at a distance, in a covert and noninvasive way, with little or none subject cooperation. In these cases, gait biometrics can be the only choice. The goal of this work is to propose a new method for gait recognition using information extracted from 2D poses estimated over video sequences. For 2D pose estimation, our method uses OpenPose, an open-source robust pose estimator, capable of real-time multi-person detection and pose estimation with high accuracy and a good computational performance. In order to assess the new proposed method, we used two public gait datasets, CASIA Gait Dataset-A and CASIA Gait Dataset-B. Both datasets have videos of a number of people walking in different directions and conditions. In our new method, the classification is carried out by a 1-NN classifier. The best results were obtained by using the chi-square distance function, which obtained 95.00% of rank-1 recognition rate on CASIA Gait Dataset-A and 94.22% of rank-1 recognition rate on CASIA Gait Dataset-B, which are comparable to state-of-the-art results.
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Hartung, D., S. Martin, and C. Busch. "Quality Estimation for Vascular Pattern Recognition." In 2011 International Conference on Hand-Based Biometrics (ICHB). IEEE, 2011. http://dx.doi.org/10.1109/ichb.2011.6094332.

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Boussaad, Leila, and Aldjia Boucetta. "Stacked Auto-Encoders Based Biometrics Recognition." In 2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI). IEEE, 2021. http://dx.doi.org/10.1109/icrami52622.2021.9585968.

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Bakina, I., and L. Mestetskiy. "Hand Shape Recognition from Natural Hand Position." In 2011 International Conference on Hand-Based Biometrics (ICHB). IEEE, 2011. http://dx.doi.org/10.1109/ichb.2011.6094317.

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Weiqi Yuan and Lantao Jing. "Hand-Shape Feature Selection and Recognition Performance Analysis." In 2011 International Conference on Hand-Based Biometrics (ICHB). IEEE, 2011. http://dx.doi.org/10.1109/ichb.2011.6094314.

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Kai Chen and D. Zhang. "Band Selection for Improvement of Dorsal Hand Recognition." In 2011 International Conference on Hand-Based Biometrics (ICHB). IEEE, 2011. http://dx.doi.org/10.1109/ichb.2011.6094333.

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Reports on the topic "Biometrics based recognition"

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Varastehpour, Soheil, Hamid Sharifzadeh, Iman Ardekani, and Abdolhossein Sarrafzadeh. Human Biometric Traits: A Systematic Review Focusing on Vascular Patterns. Unitec ePress, December 2020. http://dx.doi.org/10.34074/ocds.086.

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Authentication methods based on human traits, including fingerprint, face, iris, and palm print, have developed significantly, and currently they are mature enough to be reliably considered for human identification purposes. Recently, as a new research area, a few methods based on non-facial skin features such as vein patterns have been developed. This literature review paper explores some key biometric systems such as face recognition, iris recognition, fingerprint, and palm print, and discusses their respective advantages and disadvantages; then by providing a comprehensive analysis of these traits, and their applications, vein pattern recognition is reviewed.
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Eastman, Brittany. Legal Issues Facing Automated Vehicles, Facial Recognition, and Privacy Rights. SAE International, July 2022. http://dx.doi.org/10.4271/epr2022016.

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Facial recognition software (FRS) is a form of biometric security that detects a face, analyzes it, converts it to data, and then matches it with images in a database. This technology is currently being used in vehicles for safety and convenience features, such as detecting driver fatigue, ensuring ride share drivers are wearing a face covering, or unlocking the vehicle. Public transportation hubs can also use FRS to identify missing persons, intercept domestic terrorism, deter theft, and achieve other security initiatives. However, biometric data is sensitive and there are numerous remaining questions about how to implement and regulate FRS in a way that maximizes its safety and security potential while simultaneously ensuring individual’s right to privacy, data security, and technology-based equality. Legal Issues Facing Automated Vehicles, Facial Recognition, and Individual Rights seeks to highlight the benefits of using FRS in public and private transportation technology and addresses some of the legitimate concerns regarding its use by private corporations and government entities, including law enforcement, in public transportation hubs and traffic stops. Constitutional questions, including First, Forth, and Ninth Amendment issues, also remain unanswered. FRS is now a permanent part of transportation technology and society; with meaningful legislation and conscious engineering, it can make future transportation safer and more convenient.
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