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.

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

Made available in DSpace on 2015-03-03T15:48:40Z (GMT). No. of bitstreams: 1 FernandoP_TESE.pdf: 2701691 bytes, checksum: 2a3af30ede2c717ab23b1c7dc03a128a (MD5) Previous issue date: 2013-05-24 / This 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

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 traitement. / Including 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.