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Добірка наукової літератури з теми "Multibiometric"

Автор: Grafiati

Опубліковано: 14 березня 2023

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Статті в журналах з теми "Multibiometric"

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Jain, Anil K., and Arun Ross. "Multibiometric systems." Communications of the ACM 47, no. 1 (January 1, 2004): 34. http://dx.doi.org/10.1145/962081.962102.

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2

Ruchay, A. N. "DEVELOPMENT OF NEW ELECTIVE MULTIBIOMETRIC AUTHENTICATION." Journal of the Ural Federal District. Information security 20, no. 3 (2020): 34–41. http://dx.doi.org/10.14529/secur200304.

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The purpose of this work is the development of elective multibiometric authentication. The novelty of the work is to develop a new approach to multibiometric authentication. Depending on the availability and usability of sensors, from resistance to attacks, from diseases or injuries of users, any biometric characteristics can be selected, such as password rhythm, voice, dynam-ic signature, graphic password, etc. The paper presents the results of the development of elective multibiometric authentication based on a new approac

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3

Aftab, Anum, Farrukh Aslam Khan, Muhammad Khurram Khan, Haider Abbas, Waseem Iqbal, and Farhan Riaz. "Hand-based multibiometric systems: state-of-the-art and future challenges." PeerJ Computer Science 7 (October 7, 2021): e707. http://dx.doi.org/10.7717/peerj-cs.707.

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Анотація:

The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.

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4

Selvarani, P., and N. Malarvizhi. "Multibiometric authentication with MATLAB simulation." International Journal of Engineering & Technology 7, no. 1.7 (February 5, 2018): 47. http://dx.doi.org/10.14419/ijet.v7i1.7.9389.

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Multimodal Biometric Authentication has been used as more security purpose for establishing the user Identification, Authentication and Verification purpose. Multimodal Biometric like Fingerprint and iris are used in this research work for authentication purpose using Matlab simulation. Fingerprint recognition process like Image Enhancement, binarization, Segmentation, thinning, Minutia marking, and Matching are performed with various techniques like Histogram Equalization, Adaptive Binarization, Morphological operations, Minutiae based techniques etc.,Iris recognition process like Segmentation, Normalization, Encoding and Matching are performed with various techniques like Canny edge detection, Daughman’s Rubber sheet model, Hamming Distance etc., can be applied for Fingerprint and iris recognition for authentication purpose. Finally Performance the measure of Precision, Recall, F-Score and Accuracy has evaluated in both fingerprint and iris. It can be concluded Iris Accuracy is higher 0.96% compared with fingerprint accuracy 0.81%.

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Nair, Suresh Kumar Ramachandran, Bir Bhanu, Subir Ghosh, and Ninad S. Thakoor. "Predictive models for multibiometric systems." Pattern Recognition 47, no. 12 (December 2014): 3779–92. http://dx.doi.org/10.1016/j.patcog.2014.05.020.

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6

AlMahafzah, Harbi, and Maen Zaid AlRwashdeh. "A Survey of Multibiometric Systems." International Journal of Computer Applications 43, no. 15 (April 30, 2012): 36–43. http://dx.doi.org/10.5120/6182-8612.

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Kovaliuk, Tеtiana, Anastasiia Shevchenko, and Nataliia Kobets. "Multibiometric Identification of the Student by His Voice and Visual Biometric Indicators in the Process of Distance Education." Digital Platform: Information Technologies in Sociocultural Sphere 5, no. 1 (June 30, 2022): 90–102. http://dx.doi.org/10.31866/2617-796x.5.1.2022.261293.

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The purpose of the study is to reveal the essence of multibiometric identification of students and substantiate the feasibility of its use to improve quality and minimize errors in recognizing it using voice and visual biometric identifiers stored in audio files, video and photo images. Research Methodology. A systematic approach to determining the software requirements for a multibiometric human identification system, sound processing methods, neural network models as classifiers that identify a person by the vector of voice characteristics and methods of visual identification of a person by video stream and photo images were applied. Scientific Novelty. Methods for identifying the speaker’s voice signs, methods for identifying and registering a person by his voice signs, algorithms for visual identification of a person from her images in a video stream and from photo images based on the Viola-Jones, Eigenface and FisherFace algorithms have been further developed, and the architecture of a multibiometric identification system has been designed. Conclusions. Multibiometric identification of the student by voice and visual biometric indicators for the distance education system are offered. The system allows the extraction of acoustic characteristics from recording human language and further assignment of the obtained data to one of the predefined classes (speakers). A multilayer neural network (MNN) was used as a classifier. The classifier is trained on 43832 audio files from 108 speakers. MNN showed an accuracy of 91% in the test sample. The face in the frame is detected at the video stream frame processing stage, and the detected face is recognized. The system performed face recognition based on finding the most appropriate template of basic images stored in the database. A software system for recognizing and indexing people on video simultaneously with the identification of a person by voice has been developed, to use in the educational process for recording attendance at distance learning classes.

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Mahajan, Smita, and Asmita Deshpande. "Multibiometric Template Security using Fuzzy Vault." International Journal of Computer Applications 154, no. 3 (November 17, 2016): 21–26. http://dx.doi.org/10.5120/ijca2016912053.

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Gyaourova, Aglika, and Arun Ross. "Index Codes for Multibiometric Pattern Retrieval." IEEE Transactions on Information Forensics and Security 7, no. 2 (April 2012): 518–29. http://dx.doi.org/10.1109/tifs.2011.2172429.

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10

Kumar, Amioy, and Ajay Kumar. "A Cell-Array-Based Multibiometric Cryptosystem." IEEE Access 4 (2016): 15–25. http://dx.doi.org/10.1109/access.2015.2428277.

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Дисертації з теми "Multibiometric"

1

Dhamala, Pushpa. "Multibiometric Systems." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for telematikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18895.

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Sepasian, Mojtaba. "Multibiometric security in wireless communication systems." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/5081.

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This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims.

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3

Nandakumar, Karthik. "Multibiometric systems fusion strategies and template security /." Diss., Connect to online resource - MSU authorized users, 2008.

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Анотація:

Thesis (Ph. D.)--Michigan State University. Dept. of Computer Science and Engineering, 2008.
Title from PDF t.p. (viewed on Mar. 30, 2009) Includes bibliographical references (p. 210-228). Also issued in print.

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4

Smiley, Garrett. "Investigating the Role of Multibiometric Authentication on Professional Certification E-examination." NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/307.

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E-learning has grown to such an extent that paper-based testing is being replaced by computer-based testing otherwise known as e-exams. Because these e-exams can be delivered outside of the traditional proctored environment, additional authentication measures must be employed in order to offer similar authentication assurance as found in proctored, paper-based testing. This dissertation addressed the need for valid authentication in e-learning systems, in e-examinations in particular, and especially in professional certification e-examinations. Furthermore, this dissertation proposed a more robust method for learner authentication during e-examination taking. Finally, this dissertation extended e-learning research by comparing e-examination scores and durations of three separate groups of exam takers using different authentication methods: Online Using Username/Password (OLUP), In-Testing Center (ITC), and Online with Multibiometrics (OLMB) to better understand the role as well as the possible effect of continuous and dynamic multibiometric authentication on professional certification e-examination scores and durations. The sample used in this study was based on participants who were all professional members of a technology professional certification organization. The methodology used to collect data was a posttest only, multiple, non-equivalent groups quasi-experiment, where age, gender, and Information Technology Proficiency (ITP) were also recorded. The analyses performed in this study included pre-analysis data screening, reliability analyses for each instrument used, and the main analysis to address each hypothesis. Group affiliation, i.e. type of authentication methods, was found to have no significant effect on differences among exam scores and durations. While there was a clear path of increased mean e-examination score as authentication method was relaxed, it was evident from the analysis that these were not significant differences. Age was found to have a significant effect on exam scores where younger participants were found to have higher exam scores and lower exam durations than older participants. Gender was not found to have a significant effect on exam scores nor durations. ITP was found to have a significant effect on exam scores and durations where greater scores with the ITP instrument indicated greater exam scores and lower exam durations. This study's results can help organizations better understand the role, possible effect, and potential application of continuous and dynamic multibiometric authentication as a justifiable approach when compared with the more common authentication approach of User Identifier (UID) and password, both in professional certification e-examinations as well as in an online environment.

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Janečka, Petr. "Multimodální biometrický systém kombinující duhovku a sítnici." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234910.

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This diploma thesis focuses on multibiometric systems, specifically on biometric fusion. The thesis describes eye biometrics, i.e. recognition based on retina and iris. The key part consists of design and implementation specification of a biometric system based on retina and iris recognition.

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Giulia, Droandi. "Secure Processing of Biometric Signals in Malicious Setting." Doctoral thesis, Università di Siena, 2018. http://hdl.handle.net/11365/1061228.

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In the digital and interconnected world we live in, establishing the identity of any individual is a pressing need. Home banking, on line shopping, and social care web sites are only few examples of services where proof of identity is fundamental. Such a process can be based on "what you know" (i.g. a password), on"what you posses" (i.g. the key of a house or an ID card) or on "what you are"(ID-based, i.g. biometrics). In this thesis we focus on biometrics. Biometric recognition, or simply biometrics, refers to ``the automated recognition of individuals based on behavioral and biological characteristics'' (ISO/IEC JTC1 SC37). This method of recognition has the advantage that it does not need the memorization of any password or the possess of any token, at the same time, however, biometrics cannot be changed if compromised in any way, hence calling for the adoption of suitable protection mechanisms. In this thesis we study the development of privacy preserving protocols for biometric recognition. This is a new research field for which a number of solutions have been proposed in recent years. For efficiency reasons, the majority of those solutions are secure only against a passive adversary, that is an adversary that does not deviate from the protocol, yet tries to infer as much information as possible from the data exchanged during the protocol. On the contrary, in this thesis we look for protocols which are secure against active adversaries, that is adversaries that deliberately and arbitrarily deviate from the recognition protocol. Specifically, we propose two possible solutions using signal processing in the encrypted domain's tools. First we use a cryptographic scheme belonging to the somewhat homomorphic scheme's family and we propose both an identification and an authentication non-interactive scheme. The first protocol focuses on a one-to-many recognition task: the biometric probe of a specific individual is compared with all the probes contained in a database looking for a positive match. The second protocol, instead, considers a one to one comparison. The new probe of an enrolled individual is compared with the probe of the same individual stored during the enrollment phase. As a second contribution, we propose SEMBA: a protocol secure against active adversary for multibiometric recognition. In this case we look for a trade-off between efficiency and accuracy by combining information from two biometric traits instead of only one. The protocol relies on SPDZ, a new framework proposed by Damgård et al. which is secure also in the presence of an active adversary.

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Vertamatti, Rodolfo. "Assimetria humana no reconhecimento multibiométrico." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-16032012-151923/.

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A combinação de fontes biométricas não redundantes da multibiometria supera a precisão de cada fonte individual (monobiometria). Além do mais, dois problemas em biometria, ruído e ataques de usurpadores, podem ser minimizados pelo uso de múltiplos sensores e biometria multimodal. Entretanto, se as similaridades estão em todos traços biométricos, como em gêmeos monozigotos (MZ), o processamento de múltiplas fontes não melhora a performance. Para distinguir extrema similitude, influências epigenéticas e ambientais são mais importantes do que o DNA herdado. Esta tese examina a plasticidade fenotípica na assimetria humana como uma ferramenta para melhorar a multibiometria. A técnica de Processamento Bilateral (PB) é introduzida para analisar discordâncias em lados esquerdo e direito dos traços biométricos. PB foi testado com imagens de espectro visível e infravermelho usando Correlação Cruzada, Wavelets e Redes Neurais Artificiais. Os traços selecionados foram dentes, orelhas, íris, impressões digitais, narinas e bochechas. PB acústico também foi implementado para avaliação da assimetria vibracional durante sons vocálicos e comparado a um sistema reconhecedor de locutores com parametrização via MFCC (Mel Frequency Cepstral Coefficients) e classificado por Quantização Vetorial. Para o PB de imagens e acústico foram coletadas 20 amostras por traço biométrico durante um ano de nove irmãos masculinos adultos. Com propósito de teste, as biometrias esquerdas foram impostoras às biometrias direitas do mesmo indivíduo e vice-versa, o que levou a 18 entidades serem identificadas por traço biométrico. Resultados alcançaram identificação total em todas biometrias tratadas com PB, comparado a um máximo de 44% de identificação correta sem PB. Esta tese conclui que peculiaridades bilaterais melhoram a performance multibiométrica e podem complementar qualquer abordagem de reconhecimento.
Combination of non-redundant biometric sources in multibiometrics overcomes individual source accuracy (monobiometrics). Moreover, two problems in biometrics, noise and impostor attacks, can be minimized by the use of multi-sensor, multi-modal biometrics. However, if similarities are in all traits, as in monozygotic twins (MZ), multiple source processing does not improve performance. To distinguish extreme similitude, epigenetic and environmental influences are more important than DNA inherited. This thesis examines phenotypic plasticity in human asymmetry as a tool to ameliorate multibiometrics. Bilateral Processing (BP) technique is introduced to analyze discordances in left and right trait sides. BP was tested in visible and infrared spectrum images using Cross-Correlation, Wavelets and Artificial Neural Networks. Selected traits were teeth, ears, irises, fingerprints, nostrils and cheeks. Acoustic BP was also implemented for vibration asymmetry evaluation during voiced sounds and compared to a speaker recognition system parameterized via MFCC (Mel Frequency Cepstral Coefficients) and classified by Vector Quantization. Image and acoustic BP gathered 20 samples per biometric trait during one year from nine adult male brothers. For test purposes, left biometrics was impostor to right biometrics from the same individual and vice-versa, which led to 18 entities to be identified per trait. Results achieved total identification in all biometrics treated with BP, compared to maximum 44% of correct identification without BP. This study concludes that bilateral peculiarities improve multibiometric performance and can complement any recognition approach.

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Falguera, Fernanda Pereira Sartori [UNESP]. "Fusão de métodos baseados em minúcias e em cristas para reconhecimento de impressões digitais." Universidade Estadual Paulista (UNESP), 2008. http://hdl.handle.net/11449/98675.

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Made available in DSpace on 2014-06-11T19:29:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-07-04Bitstream added on 2014-06-13T19:38:57Z : No. of bitstreams: 1 falguera_fps_me_sjrp.pdf: 3832818 bytes, checksum: 1ca4e6b68ff66693475c6e5caed03e88 (MD5)
Biometria refere-se ao uso de características físicas (impressões digitais, íris, retina) ou comportamentais (assinatura, voz) para a identificação humana. As impressões digitais são formadas por cristas e minúcias. As cristas são linhas distribuídas paralelamente com uma orientação e um espaçamento característico e as minúcias representam os vários modos pelos quais uma crista pode se tornar descontínua. Graças a sua universalidade, unicidade e permanência, as impressões digitais tornaram-se as características biométricas mais amplamente utilizadas. Entretanto, considerar o reconhecimento automático de impressões digitais um problema totalmente resolvido é um erro muito comum. Nenhum sistema de reconhecimento de impressões digitais proposto até hoje é infalível, nenhum garante taxas de erro nulas. Imagens de baixa qualidade e com pequena área de sobreposição entre a imagem template e a imagem de consulta ainda representam um desafio para os métodos de reconhecimento de impressões digitais mais utilizados, os métodos baseados no casamento de pontos de minúcias. Uma das maneiras de superar as limitações e melhorar a acurácia de um sistema biométrico é o uso da multibiometria, isto é, a combinação de diferentes tipos de informação em um sistema de reconhecimento biométrico. Neste contexto, esta dissertação de mestrado objetiva aprimorar a acurácia dos sistemas de reconhecimento de impressões digitais por meio da fusão de métodos baseados em minúcias e em cristas. Para tanto, foram implementadas técnicas de fusão no nível de pontuação, classificação e decisão. No nível de pontuação, a fusão propiciou uma redução na taxa de erro igual (EER) de 42,53% em relação ao método mais preciso. Para o nível de classificação, a fusão significou um aumento de 75% na taxa de recuperação correta...
Biometrics refers to the use of physical (fingerprints, iris, retina) or behavioral (signature, voice) characteristics to determine the identity of a person. Fingerprints are formed by ridges and minutiae. The ridges are lines distributed in parallel with an orientation and a characteristic spacing and the minutiae represent the several ways a ridge can become discontinued. As to its universality, uniqueness and permanence, the fingerprints became the most widely used biometric characteristic. However, it is a common mistake to consider the automatic fingerprint recognition as a totally solved problem. No fingerprint recognition system proposed until now is infallible, none of them guarantee null error rates. Poor quality images and when just a small area of overlap between the template and the query images exists are still a complex challenge to the most used fingerprint recognition methods, the methods based on minutiae points matching. One of the possibilities to overcome the limitations and improve the accuracy of a biometric system is the use of multibiometrics, the combination of different kinds of information in a biometric system. In this context, this master thesis aims to improve the accuracy of fingerprint recognition systems through the fusion of minutiae based and ridge based methods. To achieve this, fusion techniques on score, rank and decision levels were implemented. For the score level, the fusion lead to a reduction of the Equal Error Rate to 42.53% compared to the most precise method. For the rank level, the fusion meant an increase of 75% in the Correct Retrieval Rate. And, in the decision level fusion the Recognition Rate changed from 99.25% to 99.75%. The results have demonstrated that the fusion of minutiae based and ridge based methods can represent a significant accuracy improvement for the fingerprint recognition systems.

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Nassar, Alaa S. N. "A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/16917.

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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. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.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. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.
Higher Committee for Education Development in Iraq

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Kisel, Andrej. "Asmens identifikavimas pagal pirštų atspaudus ir balsą." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20101230_093653-59895.

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Анотація:

Penkiose disertacijos darbo dalyse nagrinėjamos asmens identifikavimo pagal pirštų atspaudus ir balsą problemos ir siūlomi jų sprendimai. Pirštų atspaudų požymių išskyrimo algoritmų kokybės įvertinimo problemą siūloma spręsti panaudojant sintezuotus pirštų atspaudus. Darbe siūlomos žinomo pirštų atpaudų sintezės algoritmo modifikacijos, kurios leidžia sukurti piršto atspaudo vaizdą su iš anksto nustatytomis charakteristikomis ir požymiais bei pagreitina sintezės procesą. Pirštų atspaudų požymių palyginimo problemos yra aptartos ir naujas palyginimo algoritmas yra siūlomas deformuotų pirštų palyginimui. Algoritmo kokybė yra įvertinta naudojant viešai prieinamas ir vidines duomenų bazes. Naujas asmens identifikavimo pagal balsą metodas remiantis tiesinės prognozės modelio grupinės delsos požymiais ir tų požymių palyginimo metrika kokybės prasme lenkia tradicinius asmens identifikavimo pagal balsą metodus. Pirštų ir balso įrašų nepriklausomumas yra irodytas ir asmens atpažinimas pagal balsą ir pirštų atspaudus kartu yra pasiūlytas siekiant išspręsti bendras biometrinių sistemų problemas.
This dissertation focuses on person identification problems and proposes solutions to overcome those problems. First part is about fingperprint feaures extraction algorithm performance evaluaiton. Modifications to a known synthesis algorithm are proposed to make it fast and suitable for performance evaluation. Matching of deformed fingerprints is discussed in the second part of the work. New fingerprint matching algorithm that uses local structures and does not perform fingerprint alignment is proposed to match deformed fingerprints. The use of group delay features of linear prediciton model for speaker identification is proposed in the third part of the work. New similarity metric that uses group delay features is described. It is demonstrated that automatic speaker identification system with proposed features and similarity metric outperforms traditional speaker identification systems. Multibiometrics using fingerprints and voice is adressed in the last part of the dissertation.

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Книги з теми "Multibiometric"

1

Thanki, Rohit M., Vedvyas J. Dwivedi, and Komal R. Borisagar. Multibiometric Watermarking with Compressive Sensing Theory. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73183-4.

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2

Bhanu, Bir, and Venu Govindaraju, eds. Multibiometrics for Human Identification. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511921056.

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3

Borisagar, Komal R., Rohit M. Thanki, and Vedvyas J. Dwivedi. Multibiometric Watermarking with Compressive Sensing Theory: Techniques and Applications. Springer, 2019.

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4

Borisagar, Komal R., Rohit M. Thanki, and Vedvyas J. Dwivedi. Multibiometric Watermarking with Compressive Sensing Theory: Techniques and Applications. Springer, 2018.

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5

A, Karthik Nandakumar &. Anil K. Jain Ross Arun. Handbook Of Multibiometrics. Springer India, 2009.

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6

Handbook of Multibiometrics. Boston: Kluwer Academic Publishers, 2006. http://dx.doi.org/10.1007/0-387-33123-9.

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7

Handbook Of Multibiometrics. Springer, 2011.

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8

Ross, Arun A., Karthik Nandakumar, and Anil K. Jain. Handbook of Multibiometrics. Springer London, Limited, 2006.

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9

Bhanu, Bir, and Venu Govindaraju. Multibiometrics for Human Identification. Cambridge University Press, 2011.

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10

Bhanu, Bir, and Venu Govindaraju. Multibiometrics for Human Identification. Cambridge University Press, 2011.

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Частини книг з теми "Multibiometric"

1

Thanki, Rohit M., Vedvyas J. Dwivedi, and Komal R. Borisagar. "Introduction." In Multibiometric Watermarking with Compressive Sensing Theory, 1–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73183-4_1.

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2

Thanki, Rohit M., Vedvyas J. Dwivedi, and Komal R. Borisagar. "Background Information and Related Works." In Multibiometric Watermarking with Compressive Sensing Theory, 19–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73183-4_2.

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3

Thanki, Rohit M., Vedvyas J. Dwivedi, and Komal R. Borisagar. "Issues in Biometric System and Proposed Research Methodology." In Multibiometric Watermarking with Compressive Sensing Theory, 47–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73183-4_3.

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4

Thanki, Rohit M., Vedvyas J. Dwivedi, and Komal R. Borisagar. "Multibiometric Watermarking Technique Using Discrete Wavelet Transform (DWT)." In Multibiometric Watermarking with Compressive Sensing Theory, 65–89. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73183-4_4.

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5

Thanki, Rohit M., Vedvyas J. Dwivedi, and Komal R. Borisagar. "Multibiometric Watermarking Technique Using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT)." In Multibiometric Watermarking with Compressive Sensing Theory, 91–113. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73183-4_5.

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6

Thanki, Rohit M., Vedvyas J. Dwivedi, and Komal R. Borisagar. "Multibiometric Watermarking Technique Using Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD)." In Multibiometric Watermarking with Compressive Sensing Theory, 115–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73183-4_6.

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7

Thanki, Rohit M., Vedvyas J. Dwivedi, and Komal R. Borisagar. "Multibiometric Watermarking Technique Using Fast Discrete Curvelet Transform (FDCuT) and Discrete Cosine Transform (DCT)." In Multibiometric Watermarking with Compressive Sensing Theory, 137–60. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73183-4_7.

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8

Thanki, Rohit M., Vedvyas J. Dwivedi, and Komal R. Borisagar. "Conclusions and Future Work." In Multibiometric Watermarking with Compressive Sensing Theory, 161–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73183-4_8.

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9

De Marsico, Maria, Michele Nappi, and Daniel Riccio. "Multibiometric People Identification: A Self-tuning Architecture." In Advances in Biometrics, 980–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01793-3_99.

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10

Vatsa, Mayank, Richa Singh, and Afzel Noore. "Context Switching Algorithm for Selective Multibiometric Fusion." In Lecture Notes in Computer Science, 452–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-11164-8_73.

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Тези доповідей конференцій з теми "Multibiometric"

1

Vertamatti, Rodolfo, and Miguel Arjona Ramirez. "Human asymmetry in multibiometric recognition." In 2011 Ieee Workshop On Computational Intelligence In Biometrics And Identity Management - Part Of 17273 - 2011 Ssci. IEEE, 2011. http://dx.doi.org/10.1109/cibim.2011.5949214.

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2

Rattani, Ajita, D. R. Kisku, Manuele Bicego, and Massimo Tistarelli. "Robust Feature-Level Multibiometric Classification." In 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference. IEEE, 2006. http://dx.doi.org/10.1109/bcc.2006.4341631.

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3

Lazarick, R. "Multibiometric techniques and standards activities." In Proceedings 39th Annual 2005 International Carnahan Conference on Security Technology. IEEE, 2005. http://dx.doi.org/10.1109/ccst.2005.1594883.

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4

Ghouti, Lahouari, and Ahmed A. Bahjat. "Iris fusion for multibiometric systems." In 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2009. http://dx.doi.org/10.1109/isspit.2009.5407577.

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5

Nandakumar, Karthik, and Anil K. Jain. "Multibiometric Template Security Using Fuzzy Vault." In 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems. IEEE, 2008. http://dx.doi.org/10.1109/btas.2008.4699352.

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6

Silva, Arnaldo G. A., Herman M. Gomes, Hugo N. Oliveira, Paulo R. B. Lins, Diego F. S. Lima, and Leonardo V. Batista. "BioPass-UFPB: a Novel Multibiometric Database." In 2019 International Conference on Biometrics (ICB). IEEE, 2019. http://dx.doi.org/10.1109/icb45273.2019.8987313.

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7

Sharma, Renu, Sukhendu Das, and Padmaja Joshi. "Rank level fusion in multibiometric systems." In 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). IEEE, 2015. http://dx.doi.org/10.1109/ncvpripg.2015.7489952.

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Monwar, Md Maruf, and Marina L. Gavrilova. "Enhancing security through a hybrid multibiometric system." In 2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications (CIB). IEEE, 2009. http://dx.doi.org/10.1109/cib.2009.4925691.

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9

Talreja, Veeru, Matthew C. Valenti, and Nasser M. Nasrabadi. "Multibiometric secure system based on deep learning." In 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2017. http://dx.doi.org/10.1109/globalsip.2017.8308652.

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Junfeng, Li. "An Efficient Multibiometric-based Continuous Authentication Scheme." In 2022 IEEE 10th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2022. http://dx.doi.org/10.1109/iccsnt56096.2022.9972922.

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