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Dissertations / Theses on the topic 'Multimodal biometrics'
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Alsaade, Fawaz. "Score-level fusion for multimodal biometrics." Thesis, University of Hertfordshire, 2008. http://hdl.handle.net/2299/1364.
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This thesis describes research into the score-level fusion process in multimodal biometrics. The emphasis of the research is on the fusion of face and voice biometrics in the two recognition modes of verification and open-set identification. The growing interest in the use of multiple modalities in biometrics is due to its potential capabilities for eradicating certain important limitations of unimodal biometrics. One of the factors important to the accuracy of a multimodal biometric system is the choice of the technique deployed for data fusion. To address this issue, investigations are carried out into the relative performance of several statistical data fusion techniques for combining the score information in both unimodal and multimodal biometrics (i.e. speaker and/ or face verification). Another important issue associated with any multimodal technique is that of variations in the biometric data. Such variations are reflected in the corresponding biometric scores, and can thereby adversely influence the overall effectiveness of multimodal biometric recognition. To address this problem, different methods are proposed and investigated. The first approach is based on estimating the relative quality aspects of the test scores and then passing them on into the fusion process either as features or weights. The approach provides the possibility of tackling the data variations based on adjusting the weights for each of the modalities involved according to its relative quality. Another approach considered for tackling the effects of data variations is based on the use of score normalisation mechanisms. Whilst score normalisation has been widely used in voice biometrics, its effectiveness in other biometrics has not been previously investigated. This method is shown to considerably improve the accuracy of multimodal biometrics by appropriately correcting the scores from degraded modalities prior to the fusion process. The investigations in this work are also extended to the combination of score normalisation with relative quality estimation. The experimental results show that, such a combination is more effective than the use of only one of these techniques with the fusion process. The thesis presents a thorough description of the research undertaken, details the experimental results and provides a comprehensive analysis of them.
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Almayyan, Waheeda. "Performance analysis of multimodal biometric fusion." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/5998.
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Biometrics is constantly evolving technology which has been widely used in many official and commercial identification applications. In fact in recent years biometric-based authentication techniques received more attention due to increased concerns in security. Most biometric systems that are currently in use typically employ a single biometric trait. Such systems are called unibiometric systems. Despite considerable advances in recent years, there are still challenges in authentication based on a single biometric trait, such as noisy data, restricted degree of freedom, intra-class variability, non-universality, spoof attack and unacceptable error rates. Some of the challenges can be handled by designing a multimodal biometric system. Multimodal biometric systems are those which utilize or are capable of utilizing, more than one physiological or behavioural characteristic for enrolment, verification, or identification. In this thesis, we propose a novel fusion approach at a hybrid level between iris and online signature traits. Online signature and iris authentication techniques have been employed in a range of biometric applications. Besides improving the accuracy, the fusion of both of the biometrics has several advantages such as increasing population coverage, deterring spoofing activities and reducing enrolment failure. In this doctoral dissertation, we make a first attempt to combine online signature and iris biometrics. We principally explore the fusion of iris and online signature biometrics and their potential application as biometric identifiers. To address this issue, investigations is carried out into the relative performance of several statistical data fusion techniques for integrating the information in both unimodal and multimodal biometrics. We compare the results of the multimodal approach with the results of the individual online signature and iris authentication approaches. This dissertation describes research into the feature and decision fusion levels in multimodal biometrics.
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Chaw, Poh C. "Multimodal biometrics score level fusion using non-confidence information." Thesis, Nottingham Trent University, 2011. http://irep.ntu.ac.uk/id/eprint/361/.
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Multimodal biometrics refers to automatic authentication methods that depend on multiple modalities of measurable physical characteristics. It alleviates most of the restrictions of single biometrics. To combine the multimodal biometrics scores, three different categories of fusion approaches including rule based, classification based and density based approaches are available. When choosing an approach, one has to consider not only the fusion performance, but also system requirements and other circumstances. In the context of verification, classification errors arise from samples in the overlapping region (or non- confidence region) between genuine users and impostors. In score space, a further separation of the samples outside the non-confidence region does not result in further verification improvements. Therefore, information contained in the non-confidence region might be useful for improving the fusion process. Up to this point, no attempts are reported in the literature that tries to enhance the fusion process using this additional information. In this work, the use of this information is explored in rule based and density based approaches mentioned above. The first approach proposes to use the non-confidence region width as a weighting parameter for the Weighted Sum fusion rule. By doing so, the non-confidence region of the multimodal biometrics score space can be minimised. This effectively leads to a better generalisation performance than commonly used Weighted Sum rules. Furthermore, it achieves fusion performances comparable to the more complicated training based approaches. These performances are not only achieved in a wide range of bimodal biometrics experiments, but also in higher dimensional multibiometrics fusion. This method also eliminates the need for score normalization, which is required by other rule based fusion methods. The second approach proposes a new Gaussian Mixture Model based likelihood ratio fusion method. This approach suggests the application of this density based fusion to the non-confidence region only and directly reject or accept the samples in the confidence region. By applying Gaussian Mixture Model to the non-confidence ii region, a smaller and more informative region, the impact of an inaccurately chosen component number on the fusion performance can be reduced. Without tuning or using any component searching algorithm, this proposed approach achieves comparable performance to the one using specific component number searching algorithm. This successful demonstration means less resource is required whilst comparable performance can be achieved and processing time is also significantly reduced.
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Rouse, Kenneth Arthur Gilbert Juan E. "Classifying speakers using voice biometrics In a multimodal world." Auburn, Ala, 2009. http://hdl.handle.net/10415/1824.
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Ahmad, Muhammad Imran. "Feature extraction and information fusion in face and palmprint multimodal biometrics." Thesis, University of Newcastle upon Tyne, 2013. http://hdl.handle.net/10443/2128.
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Multimodal biometric systems that integrate the biometric traits from several modalities are able to overcome the limitations of single modal biometrics. Fusing the information at an earlier level by consolidating the features given by different traits can give a better result due to the richness of information at this stage. In this thesis, three novel methods are derived and implemented on face and palmprint modalities, taking advantage of the multimodal biometric fusion at feature level. The benefits of the proposed method are the enhanced capabilities in discriminating information in the fused features and capturing all of the information required to improve the classification performance. Multimodal biometric proposed here consists of several stages such as feature extraction, fusion, recognition and classification. Feature extraction gathers all important information from the raw images. A new local feature extraction method has been designed to extract information from the face and palmprint images in the form of sub block windows. Multiresolution analysis using Gabor transform and DCT is computed for each sub block window to produce compact local features for the face and palmprint images. Multiresolution Gabor analysis captures important information in the texture of the images while DCT represents the information in different frequency components. Important features with high discrimination power are then preserved by selecting several low frequency coefficients in order to estimate the model parameters. The local features extracted are fused in a new matrix interleaved method. The new fused feature vector is higher in dimensionality compared to the original feature vectors from both modalities, thus it carries high discriminating power and contains rich statistical information. The fused feature vector also has larger data points in the feature space which is advantageous for the training process using statistical methods. The underlying statistical information in the fused feature vectors is captured using GMM where several numbers of modal parameters are estimated from the distribution of fused feature vector. Maximum likelihood score is used to measure a degree of certainty to perform recognition while maximum likelihood score normalization is used for classification process. The use of likelihood score normalization is found to be able to suppress an imposter likelihood score when the background model parameters are estimated from a pool of users which include statistical information of an imposter. The present method achieved the highest recognition accuracy 97% and 99.7% when tested using FERET-PolyU dataset and ORL-PolyU dataset respectively.
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Algashaam, Faisal Mansour A. "Multispectral techniques for biometrics with focus on periocular region." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/122985/1/Faisal%20Mansour%20A_Algashaam_Thesis.pdf.
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During this study the researcher worked on human identification using the eye region called periocular and iris. The research has proposed a number of novel techniques to improve the recognition accuracy of the identification system. This work is potentially applicable to security and surveillance systems such as border control and attendance management.
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Costa, Daniel Moura Martins da. "Ensemble baseado em métodos de Kernel para reconhecimento biométrico multimodal." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-28072016-190335/.
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Com o avanço da tecnologia, as estratégias tradicionais para identificação de pessoas se tornaram mais suscetíveis a falhas, de forma a superar essas dificuldades algumas abordagens vêm sendo propostas na literatura. Dentre estas abordagens destaca-se a Biometria. O campo da Biometria abarca uma grande variedade de tecnologias usadas para identificar e verificar a identidade de uma pessoa por meio da mensuração e análise de aspectos físicos e/ou comportamentais do ser humano. Em função disso, a biometria tem um amplo campo de aplicações em sistemas que exigem uma identificação segura de seus usuários. Os sistemas biométricos mais populares são baseados em reconhecimento facial ou de impressões digitais. Entretanto, existem outros sistemas biométricos que utilizam a íris, varredura de retina, voz, geometria da mão e termogramas faciais. Nos últimos anos, o reconhecimento biométrico obteve avanços na sua confiabilidade e precisão, com algumas modalidades biométricas oferecendo bom desempenho global. No entanto, mesmo os sistemas biométricos mais avançados ainda enfrentam problemas. Recentemente, esforços têm sido realizados visando empregar diversas modalidades biométricas de forma a tornar o processo de identificação menos vulnerável a ataques. Biometria multimodal é uma abordagem relativamente nova para representação de conhecimento biométrico que visa consolidar múltiplas modalidades biométricas. A multimodalidade é baseada no conceito de que informações obtidas a partir de diferentes modalidades se complementam. Consequentemente, uma combinação adequada dessas informações pode ser mais útil que o uso de informações obtidas a partir de qualquer uma das modalidades individualmente. As principais questões envolvidas na construção de um sistema biométrico unimodal dizem respeito à definição das técnicas de extração de característica e do classificador. Já no caso de um sistema biométrico multimodal, além destas questões, é necessário definir o nível de fusão e a estratégia de fusão a ser adotada. O objetivo desta dissertação é investigar o emprego de ensemble para fusão das modalidades biométricas, considerando diferentes estratégias de fusão, lançando-se mão de técnicas avançadas de processamento de imagens (tais como transformada Wavelet, Contourlet e Curvelet) e Aprendizado de Máquina. Em especial, dar-se-á ênfase ao estudo de diferentes tipos de máquinas de aprendizado baseadas em métodos de Kernel e sua organização em arranjos de ensemble, tendo em vista a identificação biométrica baseada em face e íris. Os resultados obtidos mostraram que a abordagem proposta é capaz de projetar um sistema biométrico multimodal com taxa de reconhecimento superior as obtidas pelo sistema biométrico unimodal.With the advancement of technology, traditional strategies for identifying people become more susceptible to failure, in order to overcome these difficulties some approaches have been proposed in the literature. Among these approaches highlights the Biometrics. The field of Biometrics encompasses a wide variety of technologies used to identify and verify the person\'s identity through the measurement and analysis of physiological and behavioural aspects of the human body. As a result, biometrics has a wide field of applications in systems that require precise identification of their users. The most popular biometric systems are based on face recognition and fingerprint matching. Furthermore, there are other biometric systems that utilize iris and retinal scan, speech, face, and hand geometry. In recent years, biometrics authentication has seen improvements in reliability and accuracy, with some of the modalities offering good performance. However, even the best biometric modality is facing problems. Recently, big efforts have been undertaken aiming to employ multiple biometric modalities in order to make the authentication process less vulnerable to attacks. Multimodal biometrics is a relatively new approach to biometrics representation that consolidate multiple biometric modalities. Multimodality is based on the concept that the information obtained from different modalities complement each other. Consequently, an appropriate combination of such information can be more useful than using information from single modalities alone. The main issues involved in building a unimodal biometric System concern the definition of the feature extraction technique and type of classifier. In the case of a multimodal biometric System, in addition to these issues, it is necessary to define the level of fusion and fusion strategy to be adopted. The aim of this dissertation is to investigate the use of committee machines to fuse multiple biometric modalities, considering different fusion strategies, taking into account advanced methods in machine learning. In particular, it will give emphasis to the analyses of different types of machine learning methods based on Kernel and its organization into arrangements committee machines, aiming biometric authentication based on face, fingerprint and iris. The results showed that the proposed approach is capable of designing a multimodal biometric System with recognition rate than those obtained by the unimodal biometrics Systems.
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Pintro, Fernando. "Comit?s de Classificadores para o Reconhecimento Multibiom?trico em Dados Biom?tricos Revog?veis." Universidade Federal do Rio Grande do Norte, 2013. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18691.
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Previous issue date: 2013-05-24This work discusses the application of techniques of ensembles in multimodal recognition systems development in revocable biometrics. Biometric systems are the future identification techniques and user access control and a proof of this is the constant increases of such systems in current society. However, there is still much advancement to be developed, mainly with regard to the accuracy, security and processing time of such systems. In the search for developing more efficient techniques, the multimodal systems and the use of revocable biometrics are promising, and can model many of the problems involved in traditional biometric recognition. A multimodal system is characterized by combining different techniques of biometric security and overcome many limitations, how: failures in the extraction or processing the dataset. Among the various possibilities to develop a multimodal system, the use of ensembles is a subject quite promising, motivated by performance and flexibility that they are demonstrating over the years, in its many applications. Givin emphasis in relation to safety, one of the biggest problems found is that the biometrics is permanently related with the user and the fact of cannot be changed if compromised. However, this problem has been solved by techniques known as revocable biometrics, which consists of applying a transformation on the biometric data in order to protect the unique characteristics, making its cancellation and replacement. In order to contribute to this important subject, this work compares the performance of individual classifiers methods, as well as the set of classifiers, in the context of the original data and the biometric space transformed by different functions. Another factor to be highlighted is the use of Genetic Algorithms (GA) in different parts of the systems, seeking to further maximize their eficiency. One of the motivations of this development is to evaluate the gain that maximized ensembles systems by different GA can bring to the data in the transformed space. Another relevant factor is to generate revocable systems even more eficient by combining two or more functions of transformations, demonstrating that is possible to extract information of a similar standard through applying different transformation functions. With all this, it is clear the importance of revocable biometrics, ensembles and GA in the development of more eficient biometric systems, something that is increasingly important in the present day
O presente trabalho aborda a aplica??o de t?cnicas de comit?s de classificadores no desenvolvimento de sistemas de reconhecimento multimodais em biometrias revog?veis. Sistemas biom?tricos s?o o futuro das t?cnicas de identifica??o e controle de acesso de usu?rios, prova disso, s?o os aumentos constantes de tais sistemas na sociedade atual. Por?m, ainda existem muitos avan?os a serem desenvolvidos, principalmente no que se refere ? acur?cia, seguran?a e tempo de processamento de tais sistemas. Na busca por desenvolver t?cnicas mais eficientes, os sistemas multimodais e a utiliza??o de biometrias revog?veis mostram-se promissores, podendo contornar muitos dos problemas envolvidos no reconhecimento biom?trico tradicional. Um sistema multimodal ? caracterizado por combinar diferentes t?cnicas de seguran?a biom?trica e com isso, superar muitas limita- ??es, como: falhas de extra??o ou processamento dos dados. Dentre as v?rias possibilidades de se desenvolver um sistema multimodal, a utiliza??o de comit?s de classificadores ? um assunto bastante promissor, motivado pelo desempenho e flexibilidade que os mesmos v?m demonstrando ao longo dos anos, em suas in?meras aplica??es. Dando ?nfase em rela- ??o ? seguran?a, um dos maiores problemas encontrados se deve as biometrias estarem relacionadas permanentemente com o usu?rio e o fato de n?o poderem ser alteradas caso comprometidas. No entanto, esse problema vem sendo solucionado por t?cnicas conhecidas como biometrias revog?veis, as quais consistem em aplicar uma transforma??o sobre os dados biom?tricos de forma a proteger as caracter?sticas originais, possibilitando seu cancelamento e substitui??o. Com o objetivo de contribuir com esse importante tema, esse trabalho compara o desempenho de m?todos de classifica??es individuais, bem como conjunto de classificadores, no contexto dos dados originais e no espa?o biom?trico transformado por diferentes fun??es. Outro fator a se destacar, ? o uso de Algoritmos Gen?ticos (AGs) em diferentes partes dos sistemas, buscando maximizar ainda mais a efici?ncia dos mesmos. Uma das motiva??es desse desenvolvimento ? avaliar o ganho que os sistemas de comit?s maximizados por diferentes AGs podem trazer aos dados no espa?o transformado. Tamb?m busca-se gerar sistemas revog?veis ainda mais eficientes, atrav?s da combina??o de duas ou mais fun??es de transforma??o revog?veis, demonstrando que ? poss?vel extrair informa??es complementares de um mesmo padr?o atrav?s de tais procedimentos. Com tudo isso, fica claro a import?ncia das biometrias revog?veis, comit?s de classificadores e AGs, no desenvolvimento de sistemas biom?tricos mais eficientes, algo que se mostra cada vez mais importante nos dias atuais
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Tompkins, Richard Cortland. "Multimodal recognition using simultaneous images of iris and face with opportunistic feature selection." University of Dayton / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1312222279.
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Saleh, Mohamed Ibrahim. "Using Ears for Human Identification." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/33158.
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Biometrics includes the study of automatic methods for distinguishing human beings based on physical or behavioral traits. The problem of finding good biometric features and recognition methods has been researched extensively in recent years. Our research considers the use of ears as a biometric for human recognition. Researchers have not considered this biometric as much as others, which include fingerprints, irises, and faces. This thesis presents a novel approach to recognize individuals based on their outer ear images through spatial segmentation. This approach to recognizing is also good for dealing with occlusions. The study will present several feature extraction techniques based on spatial segmentation of the ear image. The study will also present a method for classifier fusion. Principal components analysis (PCA) is used in this research for feature extraction and dimensionality reduction. For classification, nearest neighbor classifiers are used. The research also investigates the use of ear images as a supplement to face images in a multimodal biometric system. Our base eigen-ear experiment results in an 84% rank one recognition rate, and the segmentation method yielded improvements up to 94%. Face recognition by itself, using the same approach, gave a 63% rank one recognition rate, but when complimented with ear images in a multimodal system improved to 94% rank one recognition rate.Master of Science
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Nguyen, Thanh Kien. "Human identification at a distance using iris and face." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/62876/1/Kien_Nguyen%20Thanh_Thesis.pdf.
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This research has successfully applied super-resolution and multiple modality fusion techniques to address the major challenges of human identification at a distance using face and iris. The outcome of the research is useful for security applications.
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Fatukasi, Omolara O. "Multimodal fusion of biometric experts." Thesis, University of Surrey, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493242.
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Person authentication is the process of confirming or determining a person's identity. Its purpose is to ensure that a system can only be accessed by authorised users. The Biometric method uses a person's physical or behavioural characteristics. The use of biometric characteristics is increasingly more popular as it makes unauthorised access more difficult.
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John, George Jacqueline. "Optimising multimodal fusion for biometric identification systems." Thesis, University of Kent, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418551.
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Abbadi, Laith. "Multi-factor Authentication Techniques for Video Applications over the Untrusted Internet." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23413.
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Designing a completely secure and trusted system is a challenge that still needs to be addressed. Currently, there is no online system that is: (i) easy to use, (ii) easy to deploy, (iii) inexpensive, and (iv) completely secure and trusted. The proposed authentication techniques aim to enhance security and trust for video applications in the untrustworthy online environments. We propose a transparent multimodal biometric authentication (TMBA) for video conferencing applications. The user is identified based on his/her physiological and behavioral biometrics. The technique is based on a ‘Steps-Free’ method, where the user does not have to perform any specific steps during authentication. The system will authenticate the user in a transparent way. We propose authentication techniques as an additional security layer for various ‘user-to-user’ and ‘user-to-service’ systems. For ‘user-to-user’ video conferencing systems, we propose an authentication and trust establishment procedure to identify users during a video conference. This technique enables users that have never met before to verify the identity of each other, and aims at enhancing the user’s trust in each other. For ‘user-to-service’ video conferencing systems, we propose a transparent multimodal biometric authentication technique for video banking. The technique can be added to online transaction systems as an additional security layer to enhance the security of online transactions, and to resist against web attacks, malware, and Man-In-The-Browser (MITB) attacks. In order to have a video banking conference between a user and a bank employee, the user has to be logged in to an online banking session. This requires a knowledge-based authentication. Knowledge-based authentication includes a text-based password, the ‘Challenge Questions’ method, and graphical passwords. We analyzed several graphical password schemes in terms of usability and security factors. A graphical password scheme can be an additional security layer add-on to the proposed multimodal biometric video banking system. The combined techniques provide a multimodal biometric multi-factor continuous authentication system.
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Aldosary, Saad. "Investigation of multimodal template-free biometric techniques and associated exception handling." Thesis, University of Kent, 2015. https://kar.kent.ac.uk/54805/.
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The Biometric systems are commonly used as a fundamental tool by both government and private sector organizations to allow restricted access to sensitive areas, to identify the criminals by the police and to authenticate the identification of individuals requesting to access to certain personal and confidential services. The applications of these identification tools have created issues of security and privacy relating to personal, commercial and government identities. Over the last decade, reports of increasing insecurity to the personal data of users in the public and commercial domain applications has prompted the development of more robust and sound measures to protect the personal data of users from being stolen and spoofing. The present study aimed to introduce the scheme for integrating direct and indirect biometric key generation schemes with the application of Shamir‘s secret sharing algorithm in order to address the two disadvantages: revocability of the biometric key and the exception handling of biometric modality. This study used two different approaches for key generation using Shamir‘s secret sharing scheme: template based approach for indirect key generation and template-free. The findings of this study demonstrated that the encryption key generated by the proposed system was not required to be stored in the database which prevented the attack on the privacy of the data of the individuals from the hackers. Interestingly, the proposed system was also able to generate multiple encryption keys with varying lengths. Furthermore, the results of this study also offered the flexibility of providing the multiple keys for different applications for each user. The results from this study, consequently, showed the considerable potential and prospect of the proposed scheme to generate encryption keys directly and indirectly from the biometric samples, which could enhance its success in biometric security field.
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Poinsot, Audrey. "Traitements pour la reconnaissance biométrique multimodale : algorithmes et architectures." Thesis, Dijon, 2011. http://www.theses.fr/2011DIJOS010.
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Combiner les sources d'information pour créer un système de reconnaissance biométrique multimodal permet d'atténuer les limitations de chaque caractéristique utilisée, et donne l'opportunité d'améliorer significativement les performances. Le travail présenté dans ce manuscrit a été réalisé dans le but de proposer un système de reconnaissance performant, qui réponde à des contraintes d'utilisation grand-public, et qui puisse être implanté sur un système matériel de faible coût. La solution choisie explore les possibilités apportées par la multimodalité, et en particulier par la fusion du visage et de la paume. La chaîne algorithmique propose un traitement basé sur les filtres de Gabor, ainsi qu’une fusion des scores. Une base multimodale réelle de 130 sujets acquise sans contact a été conçue et réalisée pour tester les algorithmes. De très bonnes performances ont été obtenues, et ont été confirmées sur une base virtuelle constituée de deux bases publiques (les bases AR et PolyU). L'étude approfondie de l'architecture des DSP, et les différentes implémentations qui ont été réalisées sur un composant de type TMS320c64x, démontrent qu'il est possible d'implanter le système sur un unique DSP avec des temps de traitement très courts. De plus, un travail de développement conjoint d'algorithmes et d'architectures pour l'implantation FPGA a démontré qu'il était possible de réduire significativement ces temps de traitementIncluding multiple sources of information in personal identity recognition reduces the limitations of each used characteristic and gives the opportunity to greatly improve performance. This thesis presents the design work done in order to build an efficient generalpublic recognition system, which can be implemented on a low-cost hardware platform. The chosen solution explores the possibilities offered by multimodality and in particular by the fusion of face and palmprint. The algorithmic chain consists in a processing based on Gabor filters and score fusion. A real database of 130 subjects has been designed and built for the study. High performance has been obtained and confirmed on a virtual database, which consists of two common public biometric databases (AR and PolyU). Thanks to a comprehensive study on the architecture of the DSP components and some implementations carried out on a DSP belonging to the TMS320c64x family, it has been proved that it is possible to implement the system on a single DSP with short processing times. Moreover, an algorithms and architectures development work for FPGA implementation has demonstrated that these times can be significantly reduced
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Tran, Quang Duc. "One-class classification : an approach to handle class imbalance in multimodal biometric authentication." Thesis, City, University of London, 2014. http://openaccess.city.ac.uk/19662/.
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Biometric verification is the process of authenticating a person‟s identity using his/her physiological and behavioural characteristics. It is well-known that multimodal biometric systems can further improve the authentication accuracy by combining information from multiple biometric traits at various levels, namely sensor, feature, match score and decision levels. Fusion at match score level is generally preferred due to the trade-off between information availability and fusion complexity. However, combining match scores poses a number of challenges, when treated as a two-class classification problem due to the highly imbalanced class distributions. Most conventional classifiers assume equally balanced classes. They do not work well when samples of one class vastly outnumber the samples of the other class. These challenges become even more significant, when the fusion is based on user-specific processing due to the limited availability of the genuine samples per user. This thesis aims at exploring the paradigm of one-class classification to advance the classification performance of imbalanced biometric data sets. The contributions of the research can be enumerated as follows. Firstly, a thorough investigation of the various one-class classifiers, including Gaussian Mixture Model, k-Nearest Neighbour, K-means clustering and Support Vector Data Description, has been provided. These classifiers are applied in learning the user-specific and user-independent descriptions for the biometric decision inference. It is demonstrated that the one-class classifiers are particularly useful in handling the imbalanced learning problem in multimodal biometric authentication. User-specific approach is a better alternative with respect to user-independent counterpart because it is able to overcome the so-called within-class sub-concepts problem, which arises very often in multimodal biometric systems due to the existence of user variation. Secondly, a novel adapted score fusion scheme that consists of one-class classifiers and is trained using both the genuine user and impostor samples has been proposed. This method also replaces user-independent by user-specific description to learn the characteristics of the impostor class, and thus, reducing the degree of imbalanced proportion of data for different classes. Extensive experiments are conducted on the BioSecure DS2 and XM2VTS databases to illustrate the potential of the proposed adapted score fusion scheme, which provides a relative improvement in terms of Equal Error Rate of 32% and 20% as compared to the standard sum of scores and likelihood ratio based score fusion, respectively. Thirdly, a hybrid boosting algorithm, called r-ABOC has been developed, which is capable of exploiting the natural capabilities of both the well-known Real AdaBoost and one-class classification to further improve the system performance without causing overfitting. However, unlike the conventional Real AdaBoost, the individual classifiers in the proposed schema are trained on the same data set, but with different parameter choices. This does not only generate a high diversity, which is vital to the success of r-ABOC, but also reduces the number of user-specified parameters. A comprehensive empirical study using the BioSecure DS2 and XM2VTS databases demonstrates that r-ABOC may achieve a performance gain in terms of Half Total Error Rate of up to 28% with respect to other state-of-the-art biometric score fusion techniques. Finally, a Robust Imputation based on Group Method of Data Handling (RIBG) has been proposed to handle the missing data problem in the BioSecure DS2 database. RIBG is able to provide accurate predictions of incomplete score vectors. It is observed to achieve a better performance with respect to the state-of-the-art imputation techniques, including mean, median and k-NN imputations. An important feature of RIBG is that it does not require any parameter fine-tuning, and hence, is amendable to immediate applications.
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Ejarque, Monserrate Pascual. "Normalización estadística para fusión biométrica multimodal." Doctoral thesis, Universitat Politècnica de Catalunya, 2011. http://hdl.handle.net/10803/22662.
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Los sistemas de reconocimiento biométrico utilizan ciertas características humanas como la voz, los rasgos faciales, la huella dactilar, el iris o la geometría de la mano para identificar a un individuo o verificar su identidad. Dichos sistemas se han desarrollado de forma individual para cada una de estas modalidades biométricas hasta llegar a obtener unos niveles notables de rendimiento.
Los sistemas biométricos multimodales combinan diversas modalidades en un sistema de reconocimiento único. La fusión multimodal permite mejorar los resultados obtenidos por una sola característica biométrica y hacen el sistema más robusto a ruidos e interferencias y más resistente a posibles ataques. La fusión se puede realizar a nivel de las señales adquiridas por los distintos sensores, de los parámetros obtenidos para cada modalidad, de las puntuaciones proporcionadas por expertos unimodales o de la decisión tomada por dichos expertos.
En la fusión a nivel de parámetros o puntuaciones es necesario homogeneizar las características provenientes de las diferentes modalidades biométricas de manera previa al proceso de fusión. A este proceso de homogeneización se le denomina normalización y se ha demostrado determinante en la obtención de buenos resultados de reconocimiento en los sistemas multimodales.
En esta tesis, se presentan diversos métodos de normalización que modifican la estadística de parámetros o puntuaciones. En primer lugar, se propone la normalización de la media y la varianza de las puntuaciones unimodales por medio de transformaciones afines que tienen en cuenta las estadísticas separadas de las puntuaciones de clientes e impostores. En este ámbito se presenta la normalización conjunta de medias, que iguala las medias de las puntuaciones de clientes e impostores para todas las modalidades biométricas.
También se han propuesto técnicas que minimizan la suma de las varianzas de las puntuaciones multimodales de clientes e impostores. Estas técnicas han obtenido buenos resultados en un sistema bimodal de fusión de puntuaciones de espectro de voz e imágenes faciales y se ha demostrado que una reducción de las varianzas multimodales puede comportar un mejor resultado de reconocimiento.
Por otro lado, se ha utilizado la ecualización de histograma, un método ampliamente utilizado en el tratamiento de imágenes, como técnica de normalización. Para ello, se han ecualizado los histogramas de las características unimodales sobre diversas funciones de referencia. En primer lugar, se ha utilizado el histograma de las puntuaciones de una de las modalidades biométricas como referencia en el proceso de ecualización. Esta técnica se ha mostrado especialmente efectiva al combinarla con métodos de fusión basados en la ponderación de las puntuaciones unimodales.
En una segunda aproximación, se han ecualizado las características biométricas a funciones previamente establecidas, en concreto, a una gaussiana y a una doble gaussiana. La ecualización a gaussiana ha obtenido buenos resultados como normalización en sistemas de fusión de parámetros. La ecualización de doble gaussiana se ha diseñado específicamente para la normalización de puntuaciones. Las dos gaussianas representan los lóbulos de las puntuaciones de clientes e impostores que se pueden observar en los histogramas unimodales. Se han probado diferentes variantes para determinar las varianzas de dichas gaussianas.
Las técnicas de normalización estadística presentadas en esta tesis se han probado utilizando diferentes estrategias y técnicas para la fusión, tanto para bases de datos quiméricas como para una base de datos multimodal. Además, la fusión se ha realizado a diferentes niveles, en concreto, a nivel de puntuaciones para diferentes escenarios multimodales incluyendo características de espectro voz, prosodia y caras, y a los niveles de parámetros, puntuaciones y decisión en el entorno del proyecto Agatha.
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Galdi, Chiara. "Conception et développement de systèmes biométriques multimodaux." Thesis, Paris, ENST, 2016. http://www.theses.fr/2016ENST0015/document.
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La reconnaissance biométrique a été utilisée longtemps dans des espaces confinés, généralement à l'intérieur, où les opérations de sécurité exigeaient des systèmes de haute précision, par exemple dans les postes de police, les banques, les entreprises, les aéroports. Les activités de terrain, au contraire, exigent plus de flexibilité et portabilité conduisant au développement de dispositifs pour l'acquisition des traits biométriques et des algorithmes pour la reconnaissance biométrique dans des conditions moins contraintes. L'application de la reconnaissance biométrique "portable" est limitée dans des domaines spécifiques, par exemple pour le contrôle de l'immigration, et exige des dispositifs dédiés. Pour étendre l'utilisation de la biométrie sur les appareils personnels, des tentatives ont été faites par l’intégration des scanners d'empreintes digitales dans les ordinateurs portables ou les smartphones. Mais la reconnaissance biométrique sur les appareils personnels a été utilisée seulement pour un nombre limité de tâches, comme le déverrouillage d'écrans à l'aide des empreintes digitales au lieu de mots de passe. Les activités décrites dans cette thèse se sont portées sur le développement de solutions pour la reconnaissance de l'iris sur les appareils mobiles: - Acquisition: collection de la base de données MICHE, contenant des images d'iris capturée par des appareils mobiles; - Segmentation: développement d'un algorithme de segmentation innovante; - Extraction de caractéristiques: la reconnaissance de l'iris a été combinée avec le visage et la reconnaissance du smartphone.Enfin, la reconnaissance du regard a été étudiée afin de vérifier sa possible fusion avec l'irisBiometric recognition for a long time has been used in confined spaces, usually indoor, where security-critical operations required high accuracy recognition systems, e.g. in police stations, banks, companies, airports. Field activities, on the contrary, required more portability and flexibility leading to the development of devices for less constrained biometric traits acquisition and consequently of robust algorithms for biometric recognition in less constrained conditions. However, the application of "portable" biometric recognition, was still limited in specific fields e.g. for immigration control, and still required dedicated devices. A further step would be to spread the use of biometric recognition on personal devices, as personal computers, tablets and smartphones. Some attempts in this direction were made embedding fingerprint scanners in laptops or smartphones. So far biometric recognition on personal devices has been employed just for a limited set of tasks, as to unlock the screen using fingerprints instead of passwords. The research activities described in this thesis were focused on studying and developing solutions for iris recognition on mobile devices. This topic has been analyzed in all its main phases: - Acquisition: collection of the MICHE database, containing pictures of irises acquired by mobile devices; - Segmentation: development of an innovative iris segmentation algorithm; - Feature extraction and matching: iris recognition has been combined with the face and with sensor (smartphone) recognition. Finally, the use of gaze analysis for human recognition has been investigated in order to verify its possible fusion with iris
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Darmiton, da Cunha Cavalcanti George. "Composição de biometria para sistemas multimodais de verificação de identidade pessoal." Universidade Federal de Pernambuco, 2005. https://repositorio.ufpe.br/handle/123456789/2105.
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Previous issue date: 2005Essa tese apresenta contribuições para o problema de verificação de identidade pessoal através de uma arquitetura que combina as biometrias da face, da assinatura e da dinâmica da digitação. As duas primeiras biometrias foram escolhidas por estarem integradas à vida de grande parte da sociedade e os dispositivos utilizados para capturar os padrões são comuns e de baixo custo. A terceira biometria, dinâmica da digitação, além de ser barata, é uma tecnologia transparente ao usuário. A motivação principal dessa tese é analisar estratégias de combinação de padrões para melhorar o desempenho de sistemas de identificação pessoal. Para tanto, foram identificados e investigados os seguintes pontos: (i) Verificação de assinaturas de tamanhos diferentes usando sete grupos de características: pseudo-dinâmicas, estruturais e invariantes (momentos de Hu, Maitra, Flusser, Simon e Central); (ii) Classi- ficação de faces usando Eigenbands Fusion; (iii) Verificação de autenticidade através da análise da dinâmica da digitação utilizando os tempos de pressionamento e de latência; (iv) Modelagem de uma arquitetura para combinar as três biometrias, além da realização de experimentos, visando à avaliação do desempenho; (v) Investigação do limiar de separação entre regiões que definem usuários autênticos e impostores, por classe, através da distribuição t-Student. Os resultados alcançados com o sistema combinado foram comparados com cada uma das modalidades biométricas separadamente, e mostraram que o sistema integrado conseguiu melhores taxas de acerto
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Pokorný, Karel. "Jádro multimodálního biometrického systému." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236471.
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The aim of this thesis is a design and realization of the core of multimodal biometric system. First part of the thesis sumarizes contemporary knowledge about biometric systems and about combination of their outputs. Second part introduces concept and implementation of multimodal biometric system, which uses weighted score combination and user-specific weights.
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Brown, Dane. "Investigating combinations of feature extraction and classification for improved image-based multimodal biometric systems at the feature level." Thesis, Rhodes University, 2018. http://hdl.handle.net/10962/63470.
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Multimodal biometrics has become a popular means of overcoming the limitations of unimodal biometric systems. However, the rich information particular to the feature level is of a complex nature and leveraging its potential without overfitting a classifier is not well studied. This research investigates feature-classifier combinations on the fingerprint, face, palmprint, and iris modalities to effectively fuse their feature vectors for a complementary result. The effects of different feature-classifier combinations are thus isolated to identify novel or improved algorithms. A new face segmentation algorithm is shown to increase consistency in nominal and extreme scenarios. Moreover, two novel feature extraction techniques demonstrate better adaptation to dynamic lighting conditions, while reducing feature dimensionality to the benefit of classifiers. A comprehensive set of unimodal experiments are carried out to evaluate both verification and identification performance on a variety of datasets using four classifiers, namely Eigen, Fisher, Local Binary Pattern Histogram and linear Support Vector Machine on various feature extraction methods. The recognition performance of the proposed algorithms are shown to outperform the vast majority of related studies, when using the same dataset under the same test conditions. In the unimodal comparisons presented, the proposed approaches outperform existing systems even when given a handicap such as fewer training samples or data with a greater number of classes. A separate comprehensive set of experiments on feature fusion show that combining modality data provides a substantial increase in accuracy, with only a few exceptions that occur when differences in the image data quality of two modalities are substantial. However, when two poor quality datasets are fused, noticeable gains in recognition performance are realized when using the novel feature extraction approach. Finally, feature-fusion guidelines are proposed to provide the necessary insight to leverage the rich information effectively when fusing multiple biometric modalities at the feature level. These guidelines serve as the foundation to better understand and construct biometric systems that are effective in a variety of applications.
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Cabana, Antoine. "Contribution à l'évaluation opérationnelle des systèmes biométriques multimodaux." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMC249/document.
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Le développement et la multiplication de dispositifs connectés, en particulier avec les \textit{smartphones}, nécessitent la mise en place de moyens d'authentification. Dans un soucis d'ergonomie, les industriels intègrent massivement des systèmes biométrique afin de garantir l'identité du porteur, et ce afin d'autoriser l'accès à certaines applications et fonctionnalités sensibles (paiements, e-banking, accès à des données personnelles : correspondance électronique..). Dans un soucis de garantir, une adéquation entre ces systèmes d'authentification et leur usages, la mise en œuvre d'un processus d'évaluation est nécessaire.L'amélioration des performances biométriques est un enjeux important afin de permettre l'intégration de telles solutions d'authentification dans certains environnement ayant d'importantes exigences sur les performances, particulièrement sécuritaires. Afin d'améliorer les performances et la fiabilité des authentifications, différentes sources biométriques sont susceptibles d'être utilisées dans un processus de fusion. La biométrie multimodale réalise, en particulier, la fusion des informations extraites de différentes modalités biométriquesDevelopment and spread of connected devices, in particular smartphones, requires the implementation of authentication methods. In an ergonomic concern, manufacturers integrates biometric systems in order to deal with logical control access issues. These biometric systems grant access to critical data and application (payment, e-banking, privcy concerns : emails...). Thus, evaluation processes allows to estimate the systems' suitabilty with these uses. In order to improve recognition performances, manufacturer are susceptible to perform multimodal fusion.In this thesis, the evaluation of operationnal biometric systems has been studied, and an implementation is presented. A second contribution studies the quality estimation of speech samples, in order to predict recognition performances
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Louati, Thamer. "Etude et réalisation d’un contrôle isoarchique de flux de personnes via des capteurs biométriques et infotroniques." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4308.
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Les travaux effectués dans le cadre de cette thèse porte sur le contrôle intelligent, isoarchique et multicritère de flux de personnes dans une zone fermée. Nous proposons un système de contrôle basé sur la biométrie multimodale et le RFID qui sont deux techniques complémentaires pour une sécurisation robuste et flexible du flux de personnes. La biométrie multimodale est utilisée pour une reconnaissance plus fiable des individus, et le RFID pour la sécurisation et le stockage des informations identitaires des personnes à surveiller. Ce système est complètement décentralisé et la décision concernant une demande d'accès est prise de manière autonome au niveau de chaque porte de chaque zone sous contrôle. Les entités internes participantes au processus de prise de décision répondent à des concepts exprimés via le paradigme holonique. L'ouverture automatique d'une porte est conditionnée à la conjonction de plusieurs critères. Une méthode d'aide multicritère à la décision est ainsi déployée au sein de chaque porte d'accès pour fusionner les réponses des identifications biométriques et pour traiter en temps réel les demandes d'autorisation d'accès. Tout d'abord, un état de l'art a été réalisé sur la biométrie, la multimodalité biométrique, la technologie RFID et les systèmes de contrôle d'accès physique. Ensuite, un système de contrôle intelligent, isoarchique et multicritère a été proposé, intégrant l'utilisation simultanée de la multimodalité biométrique et du RFID. Enfin, un démonstrateur du système a été implémenté dans le cadre du contrôle de flux de détenus dans une prisonThe proposed work deals with the intelligent control, isoarchic and multicriteria of people flow in a restricted area. Our proposal is a control system based on a multimodal biometrics and RFID which are considered as two secured complementary techniques for robust and flexible people flow control. Multimodal biometrics is used for more reliable individual recognitions and the RFID for securing and storing supervised individuals identity information. This system is completely decentralized and the decision related to a control access request is made autonomously at each gate of each controlled area. The internal entities which participate to the decision making process respond to the holonic paradigm concepts and principles. The automatic gate opening is conditioned with several criteria conjunction (biometrics identifications, RFID identification, access permissions, authorized paths, status of the zone at time t, etc.). A multicriteria decision aid method is thus deployed in each access gate to merge biometrics identifications responses and to automatically treat the real-time access authorization requests. First, a state of art related to the biometric recognition, the contribution of multimodal biometric, the RFID technology and the physical access control based on biometric, was done. Then, an intelligent, isoarchic and multicriteria control of people flow system was proposed, including the use of multimodal biometric and RFID. At the end, a system simulation test bed was implemented to control prisoners flow in a jail. It supports the integration of various biometrics and RFID technologies
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JENG, REN-HE, and 鄭仁和. "Multimodal Biometric Recognition: Methods and Applications." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/yr83aw.
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博士國立暨南國際大學
電機工程學系
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Unimodal biometric systems have some challenges in a variety of problems such as noisy data, intra-class variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. Some of these problems can be addressed by using multimodal biometric systems that explore the evidences presented by multiple sources of information. Aimed at improving the reliability of biometric authentication, we present a novel approach based on feature-level biometric modality fusion. This thesis proposes a two-stage transformation which produces an efficient code to feature amalgamation in which the variance of each bit is maximized and the bits are pairwise uncorrelated. We combine two contactless biometric modalities: one is face modality and another is the iris modality. For the feature extraction part, we extract both global and local features for combination which can provide complementary information, in order to excel the performance of applying single modality. Experiments in this thesis are tested on the dataset 1 (CASIA-Distance-Iris) and dataset 2 (extended Yale B face database and UBIRIS v1 eye database). The recognition system structure is divided into four parts: (i) preprocessing module, (ii) feature extraction module, (iii) fusion module, and (iv) classification and learning module. The preprocessing module detects and segments the region of interest of face and iris inside a noisy image. In the feature extraction step, we introduce a novel real local binary pattern (RLBP) histogram for global statistical features and sharpening convolutional neural network for local iris structure representation. In the feature fusion step, we use the two-stage transformation to analyze features in order to perform feature amalgamation. Finally, a classifier generated by bagged decision trees is processed to complete the classification. After comparing with several state-of-the-art multimodal biometric systems, our system achieves a equal error rate of less than 1% for verification tasks. For identification, the proposed system achieves error less than 10% using 10% feature vectors. Experimental results reveal that feature amalgamation of multimodal biometric system is better than existing feature fusion scheme, i.e., sereial/parallel feature fusion and weighted sum rule.
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"Investigating and comparing multimodal biometric techniques." Thesis, 2009. http://hdl.handle.net/10210/2538.
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M.Sc.Determining the identity of a person has become vital in today’s world. Emphasis on security has become increasingly more common in the last few decades, not only in Information Technology, but across all industries. One of the main principles of security is that a system only be accessed by a legitimate user. According to the ISO 7498/2 document [1] (an international standard which defines an information security system architecture) there are 5 pillars of information security. These are Identification/Authentication, Confidentiality, Authorization, Integrity and Non Repudiation. The very first line of security in a system is identifying and authenticating a user. This ensures that the user is who he/she claims to be, and allows only authorized individuals to access your system. Technologies have been developed that can automatically recognize a person by his unique physical features. This technology, referred to as ‘biometrics’, allows us to quickly, securely and conveniently identify an individual. Biometrics solutions have already been deployed worldwide, and it is rapidly becoming an acceptable method of identification in the eye of the public. As useful and advanced as unimodal (single biometric sample) biometric technologies are, they have their limits. Some of them aren’t completely accurate; others aren’t as secure and can be easily bypassed. Recently it has been reported to the congress of the U.S.A [2] that about 2 percent of the population in their country do not have a clear enough fingerprint for biometric use, and therefore cannot use their fingerprints for enrollment or verification. This same report recommends using a biometric system with dual (multimodal) biometric inputs, especially for large scale systems, such as airports. In this dissertation we will investigate and compare multimodal biometric techniques, in order to determine how much of an advantage lies in using this technology, over its unimodal equivalent.
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Monteiro, João Carlos de Sousa. "Multimodal Biometric Recognition under Unconstrained Settings." Doctoral thesis, 2017. https://repositorio-aberto.up.pt/handle/10216/106951.
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Monteiro, João Carlos de Sousa. "Multimodal Biometric Recognition under Unconstrained Settings." Tese, 2017. https://repositorio-aberto.up.pt/handle/10216/106951.
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"Decision fusion in a multimodal biometric system." 2004. http://library.cuhk.edu.hk/record=b5891972.
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Lau, Chun Wai.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.
Includes bibliographical references (leaves 119-123).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Overview --- p.1
Chapter 1.2 --- Multimodal Biometric Systems --- p.3
Chapter 1.3 --- Objectives --- p.7
Chapter 1.4 --- Thesis Outline --- p.7
Chapter 2 --- Background --- p.9
Chapter 2.1 --- Decision Fusions in Multimodal Biometric Systems --- p.10
Chapter 2.2 --- Fuzzy Logic --- p.15
Chapter 2.2.1 --- Fuzzy Sets and Their Operations --- p.15
Chapter 2.2.2 --- Fuzzy Rules --- p.17
Chapter 2.2.3 --- Defuzzification --- p.18
Chapter 2.2.4 --- Applications of Fuzzy Logic --- p.19
Chapter 2.3 --- Demspter-Shafer Theory of Evidence --- p.20
Chapter 2.3.1 --- Belief and Plausibility --- p.20
Chapter 2.3.2 --- Dempster's Rule of Combination --- p.21
Chapter 2.3.3 --- Applications of Dempster-Shafer Theory of Evidence --- p.22
Chapter 2.4 --- Chapter Summary --- p.23
Chapter 3 --- Biometric Modalities --- p.24
Chapter 3.1 --- Speaker Verification --- p.24
Chapter 3.1.1 --- Data Collection --- p.25
Chapter 3.1.2 --- Experiment and Results --- p.26
Chapter 3.2 --- Face Identification --- p.27
Chapter 3.2.1 --- Data Collection --- p.28
Chapter 3.2.2 --- Experiment and Results --- p.29
Chapter 3.3 --- Fingerprint Verification --- p.35
Chapter 3.3.1 --- Data Collection --- p.36
Chapter 3.3.2 --- Experiment and Results --- p.37
Chapter 3.4 --- Chapter Summary --- p.38
Chapter 4 --- Baseline Fusions --- p.39
Chapter 4.1 --- Majority Voting --- p.40
Chapter 4.2 --- Fusion by Weighted Average Scores --- p.45
Chapter 4.3 --- Comparison of Fusion by Majority Voting and Fusion by Weighted Average Scores --- p.51
Chapter 4.4 --- Chapter Summary --- p.53
Chapter 5 --- Fuzzy Logic Decision Fusion --- p.54
Chapter 5.1 --- Introduction --- p.55
Chapter 5.2 --- Fuzzy Inference System --- p.56
Chapter 5.2.1 --- Input Fuzzy Variables and Fuzzy Sets for Face Biometric --- p.56
Chapter 5.2.2 --- Input Fuzzy Variables and Fuzzy Sets for Fingerprint Biometric --- p.59
Chapter 5.2.3 --- Output Fuzzy Variables and Fuzzy Sets --- p.62
Chapter 5.2.4 --- Fuzzy Rules for Face Biometric --- p.63
Chapter 5.2.5 --- Fuzzy Rules for Fingerprint Biometric --- p.64
Chapter 5.3 --- Experiments with Fuzzy Logic Fusion --- p.66
Chapter 5.4 --- Significance Testing --- p.71
Chapter 5.5 --- Comparison of Fuzzy Logic Fusion and Weighted Average Scores --- p.74
Chapter 5.6 --- Testing of Fuzzy Rule Properties --- p.76
Chapter 5.6.1 --- Experiment 1 --- p.77
Chapter 5.6.2 --- Experiment 2 --- p.80
Chapter 5.6.3 --- Experiment 3 --- p.83
Chapter 5.6.4 --- Comparison of Results --- p.86
Chapter 5.7 --- Chapter Summary --- p.86
Chapter 6 --- Decision Fusion Based on Dempster-Shafer Theory of Evi- dence --- p.88
Chapter 6.1 --- Introduction --- p.89
Chapter 6.2 --- Framework of Fusion Based on Dempster-Shafer Theory of Evidence --- p.90
Chapter 6.2.1 --- Evidences for Biometric Systems --- p.91
Chapter 6.2.2 --- Intra-Modality Combination --- p.95
Chapter 6.2.3 --- Inter-Modality Combination --- p.97
Chapter 6.3 --- Experiments with Fusion Based on Dempster-Shafer Theory of Evidence --- p.99
Chapter 6.4 --- Significance Testing --- p.103
Chapter 6.5 --- Comparison of Fusion Based on Dempster-Shafer Theory of Evidence and Weighted Average Scores --- p.106
Chapter 6.6 --- Comparison of Fusion Based on Dempster-Shafer Theory of Evidence and Fuzzy Logic Fusion --- p.108
Chapter 6.7 --- Chapter Summary --- p.110
Chapter 7 --- Conclusions --- p.112
Chapter 7.1 --- Summary --- p.112
Chapter 7.2 --- Contributions --- p.115
Chapter 7.3 --- Future Work --- p.117
Bibliography --- p.119
Chapter A --- Fuzzy Rules --- p.124
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Esteves, Rui Cardoso. "Mobile multimodal biometric identification for african communities." Master's thesis, 2015. https://repositorio-aberto.up.pt/handle/10216/79575.
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Esteves, Rui Cardoso. "Mobile multimodal biometric identification for african communities." Dissertação, 2015. https://repositorio-aberto.up.pt/handle/10216/79575.
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"Classification and fusion methods for multimodal biometric authentication." 2007. http://library.cuhk.edu.hk/record=b5893313.
Full textAbstract:
Ouyang, Hua.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.
Includes bibliographical references (leaves 81-89).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Biometric Authentication --- p.1
Chapter 1.2 --- Multimodal Biometric Authentication --- p.2
Chapter 1.2.1 --- Combination of Different Biometric Traits --- p.3
Chapter 1.2.2 --- Multimodal Fusion --- p.5
Chapter 1.3 --- Audio-Visual Bi-modal Authentication --- p.6
Chapter 1.4 --- Focus of This Research --- p.7
Chapter 1.5 --- Organization of This Thesis --- p.8
Chapter 2 --- Audio-Visual Bi-modal Authentication --- p.10
Chapter 2.1 --- Audio-visual Authentication System --- p.10
Chapter 2.1.1 --- Why Audio and Mouth? --- p.10
Chapter 2.1.2 --- System Overview --- p.11
Chapter 2.2 --- XM2VTS Database --- p.12
Chapter 2.3 --- Visual Feature Extraction --- p.14
Chapter 2.3.1 --- Locating the Mouth --- p.14
Chapter 2.3.2 --- Averaged Mouth Images --- p.17
Chapter 2.3.3 --- Averaged Optical Flow Images --- p.21
Chapter 2.4 --- Audio Features --- p.23
Chapter 2.5 --- Video Stream Classification --- p.23
Chapter 2.6 --- Audio Stream Classification --- p.25
Chapter 2.7 --- Simple Fusion --- p.26
Chapter 3 --- Weighted Sum Rules for Multi-modal Fusion --- p.27
Chapter 3.1 --- Measurement-Level Fusion --- p.27
Chapter 3.2 --- Product Rule and Sum Rule --- p.28
Chapter 3.2.1 --- Product Rule --- p.28
Chapter 3.2.2 --- Naive Sum Rule (NS) --- p.29
Chapter 3.2.3 --- Linear Weighted Sum Rule (WS) --- p.30
Chapter 3.3 --- Optimal Weights Selection for WS --- p.31
Chapter 3.3.1 --- Independent Case --- p.31
Chapter 3.3.2 --- Identical Case --- p.33
Chapter 3.4 --- Confidence Measure Based Fusion Weights --- p.35
Chapter 4 --- Regularized k-Nearest Neighbor Classifier --- p.39
Chapter 4.1 --- Motivations --- p.39
Chapter 4.1.1 --- Conventional k-NN Classifier --- p.39
Chapter 4.1.2 --- Bayesian Formulation of kNN --- p.40
Chapter 4.1.3 --- Pitfalls and Drawbacks of kNN Classifiers --- p.41
Chapter 4.1.4 --- Metric Learning Methods --- p.43
Chapter 4.2 --- Regularized k-Nearest Neighbor Classifier --- p.46
Chapter 4.2.1 --- Metric or Not Metric? --- p.46
Chapter 4.2.2 --- Proposed Classifier: RkNN --- p.47
Chapter 4.2.3 --- Hyperkernels and Hyper-RKHS --- p.49
Chapter 4.2.4 --- Convex Optimization of RkNN --- p.52
Chapter 4.2.5 --- Hyper kernel Construction --- p.53
Chapter 4.2.6 --- Speeding up RkNN --- p.56
Chapter 4.3 --- Experimental Evaluation --- p.57
Chapter 4.3.1 --- Synthetic Data Sets --- p.57
Chapter 4.3.2 --- Benchmark Data Sets --- p.64
Chapter 5 --- Audio-Visual Authentication Experiments --- p.68
Chapter 5.1 --- Effectiveness of Visual Features --- p.68
Chapter 5.2 --- Performance of Simple Sum Rule --- p.71
Chapter 5.3 --- Performances of Individual Modalities --- p.73
Chapter 5.4 --- Identification Tasks Using Confidence-based Weighted Sum Rule --- p.74
Chapter 5.4.1 --- Effectiveness of WS_M_C Rule --- p.75
Chapter 5.4.2 --- WS_M_C v.s. WS_M --- p.76
Chapter 5.5 --- Speaker Identification Using RkNN --- p.77
Chapter 6 --- Conclusions and Future Work --- p.78
Chapter 6.1 --- Conclusions --- p.78
Chapter 6.2 --- Important Follow-up Works --- p.80
Bibliography --- p.81
Chapter A --- Proof of Proposition 3.1 --- p.90
Chapter B --- Proof of Proposition 3.2 --- p.93
33
Sentosa, Kevin Octavius, and 薛有強. "Performance Evaluation of Score Level Fusion in Multimodal Biometric Systems." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/24339197005670279813.
Full textAbstract:
碩士國立臺灣科技大學
資訊工程系
96
In a multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. In this paper we examined the performance of sum rule-based score level fusion and Support Vector Machines (SVM)-based score level fusion. Three biometric characteristics were considered in this study: fingerprint, face, and finger vein. We also proposed a new robust normalization scheme which is derived from min-max normalization scheme. Experiments on four different multimodal databases suggest that integrating the proposed scheme in sum rule-based fusion and SVM-based fusion leads to consistently high accuracy. The performance of simple sum rule preceded by our normalization scheme is comparable to another approach which is based on the estimation of matching scores densities. Comparison between experimental results on sum rule-based fusion and SVM-based fusion reveals that SVM-based fusion could attain better performance compared to sum rule-based fusion, provided that the kernel and its parameters have been carefully selected.
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Alshanketi, Faisal. "Enhanced usability, resilience, and accuracy in mobile keystroke dynamic biometric authentication." Thesis, 2018. https://dspace.library.uvic.ca//handle/1828/10093.
Full textAbstract:
With the progress achieved to this date in mobile computing technologies, mobile devices are increasingly being used to store sensitive data and perform security-critical transactions and services. However, the protection available on these devices is still lagging behind. The primary and often only protection mechanism in these devices is authentication using a password or a PIN. Passwords are notoriously known to be a weak
authentication mechanism, no matter how complex the underlying format is. Mobile authentication can be strengthened by extracting and analyzing keystroke dynamic biometric from supplied passwords. In this thesis, I identified gaps in the literature, and investigated new models and mechanisms to improve accuracy, usability and resilience against statistical forgeries for mobile keystroke dynamic biometric authentication. Accuracy is investigated through cost sensitive learning and sampling, and by comparing the strength of different classifiers. Usability is improved by introducing a new approach for typo handling in the authentication model. Resilience against statistical attacks is achieved by introducing a new multimodal approach combining fixed and variable keystroke dynamic biometric passwords, in which two different fusion models are studied. Experimental evaluation using several datasets, some publicly available and others collected locally, yielded encouraging performance results in terms of accuracy, usability, and resistance against statistical attacks.Graduate
2019-09-25
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"Composição de biometrias para sistemas multimodais de verificação de identidade pessol." Tese, Biblioteca Digital de Teses e Dissertações da UFPE, 2005. http://www.bdtd.ufpe.br/tedeSimplificado//tde_busca/arquivo.php?codArquivo=402.
Full text
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Rokita, Joanna. "Multimodal biometric system based on face and hand images taken by a cell phone." Thesis, 2008. http://spectrum.library.concordia.ca/975752/1/MR40952.pdf.
Full textAbstract:
One of the methods to improve the recognition rate of humans is multimodal biometrics, which is based on more than one physiological or behavioral characteristics to identify an individual. Multimodal biometrics improves not only the performance, but also nonuniversality and spoofing that are commonly encountered in unibiometric systems. In this thesis, we built a multibiometric system that works on face and hand images taken by a camera built into a cell phone. The multimodal fusion is done at the feature extraction level. The nine facial models are built according to the number of features / points extracted from the face. Active shape models method is applied in order to find the concatenated string of facial points in the eyes, nose, and mouth areas. The face feature vector is constructed by applying Gabor filter to the image and extracting the key points found by an active shape model. The hand feature vector contains nine geometric measurements, including heights and widths of four fingers, and the width of the palm. Support vector machine is used as a classifier for a multimodal approach. One SVM machine is built for each person in the database to distinguish that person from the others. The database contains 113 individuals. As the experiments show, the best accuracy of up to 99.82% has been achieved for the multibiometric model combining 8 eye points, 4 nose points, and 9 hand features.
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Al-Waisy, Alaa S., Rami S. R. Qahwaji, Stanley S. Ipson, Shumoos Al-Fahdawi, and Tarek A. M. Nagem. "A multi-biometric iris recognition system based on a deep learning approach." 2017. http://hdl.handle.net/10454/15682.
Full textAbstract:
YesMultimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person.