Investigation of Multimodal Template-Free Biometric Techniques and Associated Exception Handling

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

Investigation of iris recognition in the visible spectrum

mong the biometric systems that have been developed so far, iris recognition systems have emerged as being one of the most reliable. In iris recognition, most of the research was conducted on operation under near infrared illumination. For unconstrained scenarios of iris recognition systems, the iris images are captured under visible light spectrum and therefore incorporate various types of imperfections. In this thesis the merits of fusing information from various sources for improving the state of the art accuracies of colour iris recognition systems is evaluated. An investigation of how fundamentally different fusion strategies can increase the degree of choice available in achieving certain performance criteria is conducted. Initially, simple fusion mechanisms are employed to increase the accuracy of an iris recognition system and then more complex fusion architectures are elaborated to further enhance the biometric system’s accuracy. In particular, the design process of the iris recognition system with reduced constraints is carried out using three different fusion approaches: multi-algorithmic, texture and colour fusion and multiple classifier systems. In the first approach, one novel iris feature extraction methodology is proposed and a multi-algorithmic iris recognition system using score fusion, composed of 3 individual systems, is benchmarked. In the texture and colour fusion approach, the advantages of fusing information from the iris texture with data extracted from the eye colour are illustrated. Finally, the multiple classifier systems approach investigates how the robustness and practicability of an iris recognition system operating on visible spectrum images can be enhanced by training individual classifiers on different iris features. Besides the various fusion techniques explored, an iris segmentation algorithm is proposed and a methodology for finding which colour channels from a colour space reveal the most discriminant information from the iris texture is introduced. The contributions presented in this thesis indicate that iris recognition systems that operate on visible spectrum images can be designed to operate with an accuracy required by a particular application scenario. Also, the iris recognition systems developed in the present study are suitable for mobile and embedded implementations

Privacy-Preserving Biometric Authentication

Biometric-based authentication provides a highly accurate means of authentication without requiring the user to memorize or possess anything. However, there are three disadvantages to the use of biometrics in authentication; any compromise is permanent as it is impossible to revoke biometrics; there are significant privacy concerns with the loss of biometric data; and humans possess only a limited number of biometrics, which limits how many services can use or reuse the same form of authentication. As such, enhancing biometric template security is of significant research interest. One of the methodologies is called cancellable biometric template which applies an irreversible transformation on the features of the biometric sample and performs the matching in the transformed domain. Yet, this is itself susceptible to specific classes of attacks, including hill-climb, pre-image, and attacks via records multiplicity. This work has several outcomes and contributions to the knowledge of privacy-preserving biometric authentication. The first of these is a taxonomy structuring the current state-of-the-art and provisions for future research. The next of these is a multi-filter framework for developing a robust and secure cancellable biometric template, designed specifically for fingerprint biometrics. This framework is comprised of two modules, each of which is a separate cancellable fingerprint template that has its own matching and measures. The matching for this is based on multiple thresholds. Importantly, these methods show strong resistance to the above-mentioned attacks. Another of these outcomes is a method that achieves a stable performance and can be used to be embedded into a Zero-Knowledge-Proof protocol. In this novel method, a new strategy was proposed to improve the recognition error rates which is privacy-preserving in the untrusted environment. The results show promising performance when evaluated on current datasets

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Iris Recognition: Robust Processing, Synthesis, Performance Evaluation and Applications

The popularity of iris biometric has grown considerably over the past few years. It has resulted in the development of a large number of new iris processing and encoding algorithms. In this dissertation, we will discuss the following aspects of the iris recognition problem: iris image acquisition, iris quality, iris segmentation, iris encoding, performance enhancement and two novel applications.;The specific claimed novelties of this dissertation include: (1) a method to generate a large scale realistic database of iris images; (2) a crosspectral iris matching method for comparison of images in color range against images in Near-Infrared (NIR) range; (3) a method to evaluate iris image and video quality; (4) a robust quality-based iris segmentation method; (5) several approaches to enhance recognition performance and security of traditional iris encoding techniques; (6) a method to increase iris capture volume for acquisition of iris on the move from a distance and (7) a method to improve performance of biometric systems due to available soft data in the form of links and connections in a relevant social network

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

Detecção de ataques de apresentação por faces em dispositivos móveis

Orientadores: Anderson de Rezende Rocha, Fernanda Alcântara AndalóDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Com o crescimento e popularização de tecnologias de autenticação biométrica, tais como aquelas baseadas em reconhecimento facial, aumenta-se também a motivação para se explorar ataques em nível de sensor de captura ameaçando a eficácia dessas aplicações em cenários reais. Um desses ataques se dá quando um impostor, desejando destravar um celular alheio, busca enganar o sistema de reconhecimento facial desse dispositivo apresentando a ele uma foto do usuário alvo. Neste trabalho, estuda-se o problema de detecção automática de ataques de apresentação ao reconhecimento facial em dispositivos móveis, considerando o caso de uso de destravamento rápido e as limitações desses dispositivos. Não se assume o uso de sensores adicionais, ou intervenção consciente do usuário, dependendo apenas da imagem capturada pela câmera frontal em todos os processos de decisão. Contribuições foram feitas em relação a diferentes aspectos do problema. Primeiro, foi coletada uma base de dados de ataques de apresentação chamada RECOD-MPAD, que foi especificamente projetada para o cenário alvo, possuindo variações realistas de iluminação, incluindo sessões ao ar livre e de baixa luminosidade, ao contrário das bases públicas disponíveis atualmente. Em seguida, para enriquecer o entendimento do que se pode esperar de métodos baseados puramente em software, adota-se uma abordagem em que as características determinantes para o problema são aprendidas diretamente dos dados a partir de redes convolucionais, diferenciando-se de abordagens tradicionais baseadas em conhecimentos específicos de aspectos do problema. São propostas três diferentes formas de treinamento da rede convolucional profunda desenvolvida para detectar ataques de apresentação: treinamento com faces inteiras e alinhadas, treinamento com patches (regiões de interesse) de resolução variável, e treinamento com uma função objetivo projetada especificamente para o problema. Usando uma arquitetura leve como núcleo da nossa rede, certifica-se que a solução desenvolvida pode ser executada diretamente em celulares disponíveis no mercado no ano de 2017. Adicionalmente, é feita uma análise que considera protocolos inter-fatores e disjuntos de usuário, destacando-se alguns dos problemas com bases de dados e abordagens atuais. Experimentos no benchmark OULU-NPU, proposto recentemente e usado em uma competição internacional, sugerem que os métodos propostos se comparam favoravelmente ao estado da arte, e estariam entre os melhores na competição, mesmo com a condição de pouco uso de memória e recursos computacionais limitados. Finalmente, para melhor adaptar a solução a cada usuário, propõe-se uma forma efetiva de usar uma galeria de dados do usuário para adaptar os modelos ao usuário e ao dispositivo usado, aumentando sua eficácia no cenário operacionalAbstract: With the widespread use of biometric authentication systems, such as those based on face recognition, comes the exploitation of simple attacks at the sensor level that can undermine the effectiveness of these technologies in real-world setups. One example of such attack takes place when an impostor, aiming at unlocking someone else's smartphone, deceives the device¿s built-in face recognition system by presenting a printed image of the genuine user's face. In this work, we study the problem of automatically detecting presentation attacks against face authentication methods in mobile devices, considering the use-case of fast device unlocking and hardware constraints of such devices. We do not assume the existence of any extra sensors or user intervention, relying only on the image captured by the device¿s frontal camera. Our contributions lie on multiple aspects of the problem. Firstly, we collect RECOD-MPAD, a new presentation-attack dataset that is tailored to the mobile-device setup, and is built to have real-world variations in lighting, including outdoors and low-light sessions, in contrast to existing public datasets. Secondly, to enrich the understanding of how far we can go with purely software-based methods when tackling this problem, we adopt a solely data-driven approach ¿ differently from handcrafted methods in prior art that focus on specific aspects of the problem ¿ and propose three different ways of training a deep convolutional neural network to detect presentation attacks: training with aligned faces, training with multi-resolution patches, and training with a multi-objective loss function crafted specifically to the problem. By using a lightweight architecture as the core of our network, we ensure that our solution can be efficiently embedded in modern smartphones in the market at the year of 2017. Additionally, we provide a careful analysis that considers several user-disjoint and cross-factor protocols, highlighting some of the problems with current datasets and approaches. Experiments with the OULU-NPU benchmark, which was used recently in an international competition, suggest that our methods are among the top performing ones. Finally, to further enhance the model's efficacy and discriminability in the target setup of user authentication for mobile devices, we propose a method that leverages the available gallery of user data in the device and adapts the method decision-making process to the user's and device¿s own characteristicsMestradoCiência da ComputaçãoMestre em Ciência da Computaçã