Academic literature on the topic 'Multibiometric'

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.

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

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.

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.

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.

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.<br>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.

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)<br>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...<br>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.

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.<br>Higher Committee for Education Development in Iraq

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.<br>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.