26 research outputs found
Restrictive Voting Technique for Faces Spoofing Attack
Face anti-spoofing has become widely used due to the increasing use of biometric authentication systems that rely on facial recognition. It is a critical issue in biometric authentication systems that aim to prevent unauthorized access. In this paper, we propose a modified version of majority voting that ensembles the votes of six classifiers for multiple video chunks to improve the accuracy of face anti-spoofing. Our approach involves sampling sub-videos of 2 seconds each with a one-second overlap and classifying each sub-video using multiple classifiers. We then ensemble the classifications for each sub-video across all classifiers to decide the complete video classification. We focus on the False Acceptance Rate (FAR) metric to highlight the importance of preventing unauthorized access. We evaluated our method using the Replay Attack dataset and achieved a zero FAR. We also reported the Half Total Error Rate (HTER) and Equal Error Rate (EER) and gained a better result than most state-of-the-art methods. Our experimental results show that our proposed method significantly reduces the FAR, which is crucial for real-world face anti-spoofing applications
Feature Fusion for Fingerprint Liveness Detection
For decades, fingerprints have been the most widely used biometric trait in identity
recognition systems, thanks to their natural uniqueness, even in rare cases such as
identical twins. Recently, we witnessed a growth in the use of fingerprint-based
recognition systems in a large variety of devices and applications. This, as a consequence,
increased the benefits for offenders capable of attacking these systems. One
of the main issues with the current fingerprint authentication systems is that, even
though they are quite accurate in terms of identity verification, they can be easily
spoofed by presenting to the input sensor an artificial replica of the fingertip skin’s
ridge-valley patterns.
Due to the criticality of this threat, it is crucial to develop countermeasure
methods capable of facing and preventing these kind of attacks. The most effective
counter–spoofing methods are those trying to distinguish between a "live" and a
"fake" fingerprint before it is actually submitted to the recognition system. According
to the technology used, these methods are mainly divided into hardware and software-based
systems. Hardware-based methods rely on extra sensors to gain more pieces
of information regarding the vitality of the fingerprint owner. On the contrary,
software-based methods merely rely on analyzing the fingerprint images acquired
by the scanner. Software-based methods can then be further divided into dynamic,
aimed at analyzing sequences of images to capture those vital signs typical of a real
fingerprint, and static, which process a single fingerprint impression. Among these
different approaches, static software-based methods come with three main benefits.
First, they are cheaper, since they do not require the deployment of any additional
sensor to perform liveness detection. Second, they are faster since the information
they require is extracted from the same input image acquired for the identification
task. Third, they are potentially capable of tackling novel forms of attack through an
update of the software. The interest in this type of counter–spoofing methods is at the basis of this
dissertation, which addresses the fingerprint liveness detection under a peculiar
perspective, which stems from the following consideration. Generally speaking, this
problem has been tackled in the literature with many different approaches. Most of
them are based on first identifying the most suitable image features for the problem
in analysis and, then, into developing some classification system based on them. In
particular, most of the published methods rely on a single type of feature to perform
this task. Each of this individual features can be more or less discriminative and often
highlights some peculiar characteristics of the data in analysis, often complementary
with that of other feature. Thus, one possible idea to improve the classification
accuracy is to find effective ways to combine them, in order to mutually exploit their
individual strengths and soften, at the same time, their weakness. However, such a
"multi-view" approach has been relatively overlooked in the literature.
Based on the latter observation, the first part of this work attempts to investigate
proper feature fusion methods capable of improving the generalization and robustness
of fingerprint liveness detection systems and enhance their classification strength.
Then, in the second part, it approaches the feature fusion method in a different way,
that is by first dividing the fingerprint image into smaller parts, then extracting an
evidence about the liveness of each of these patches and, finally, combining all these
pieces of information in order to take the final classification decision.
The different approaches have been thoroughly analyzed and assessed by comparing
their results (on a large number of datasets and using the same experimental
protocol) with that of other works in the literature. The experimental results discussed
in this dissertation show that the proposed approaches are capable of obtaining
state–of–the–art results, thus demonstrating their effectiveness
Análise de propriedades intrĂnsecas e extrĂnsecas de amostras biomĂ©tricas para detecção de ataques de apresentação
Orientadores: Anderson de Rezende Rocha, HĂ©lio PedriniTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os recentes avanços nas áreas de pesquisa em biometria, forense e segurança da informação trouxeram importantes melhorias na eficácia dos sistemas de reconhecimento biomĂ©tricos. No entanto, um desafio ainda em aberto Ă© a vulnerabilidade de tais sistemas contra ataques de apresentação, nos quais os usuários impostores criam amostras sintĂ©ticas, a partir das informações biomĂ©tricas originais de um usuário legĂtimo, e as apresentam ao sensor de aquisição procurando se autenticar como um usuário válido. Dependendo da modalidade biomĂ©trica, os tipos de ataque variam de acordo com o tipo de material usado para construir as amostras sintĂ©ticas. Por exemplo, em biometria facial, uma tentativa de ataque Ă© caracterizada quando um usuário impostor apresenta ao sensor de aquisição uma fotografia, um vĂdeo digital ou uma máscara 3D com as informações faciais de um usuário-alvo. Em sistemas de biometria baseados em Ăris, os ataques de apresentação podem ser realizados com fotografias impressas ou com lentes de contato contendo os padrões de Ăris de um usuário-alvo ou mesmo padrões de textura sintĂ©ticas. Nos sistemas biomĂ©tricos de impressĂŁo digital, os usuários impostores podem enganar o sensor biomĂ©trico usando rĂ©plicas dos padrões de impressĂŁo digital construĂdas com materiais sintĂ©ticos, como látex, massa de modelar, silicone, entre outros. Esta pesquisa teve como objetivo o desenvolvimento de soluções para detecção de ataques de apresentação considerando os sistemas biomĂ©tricos faciais, de Ăris e de impressĂŁo digital. As linhas de investigação apresentadas nesta tese incluem o desenvolvimento de representações baseadas nas informações espaciais, temporais e espectrais da assinatura de ruĂdo; em propriedades intrĂnsecas das amostras biomĂ©tricas (e.g., mapas de albedo, de reflectância e de profundidade) e em tĂ©cnicas de aprendizagem supervisionada de caracterĂsticas. Os principais resultados e contribuições apresentadas nesta tese incluem: a criação de um grande conjunto de dados publicamente disponĂvel contendo aproximadamente 17K videos de simulações de ataques de apresentações e de acessos genuĂnos em um sistema biomĂ©trico facial, os quais foram coletados com a autorização do ComitĂŞ de Ética em Pesquisa da Unicamp; o desenvolvimento de novas abordagens para modelagem e análise de propriedades extrĂnsecas das amostras biomĂ©tricas relacionadas aos artefatos que sĂŁo adicionados durante a fabricação das amostras sintĂ©ticas e sua captura pelo sensor de aquisição, cujos resultados de desempenho foram superiores a diversos mĂ©todos propostos na literature que se utilizam de mĂ©todos tradicionais de análise de images (e.g., análise de textura); a investigação de uma abordagem baseada na análise de propriedades intrĂnsecas das faces, estimadas a partir da informação de sombras presentes em sua superfĂcie; e, por fim, a investigação de diferentes abordagens baseadas em redes neurais convolucionais para o aprendizado automático de caracterĂsticas relacionadas ao nosso problema, cujos resultados foram superiores ou competitivos aos mĂ©todos considerados estado da arte para as diferentes modalidades biomĂ©tricas consideradas nesta tese. A pesquisa tambĂ©m considerou o projeto de eficientes redes neurais com arquiteturas rasas capazes de aprender caracterĂsticas relacionadas ao nosso problema a partir de pequenos conjuntos de dados disponĂveis para o desenvolvimento e a avaliação de soluções para a detecção de ataques de apresentaçãoAbstract: Recent advances in biometrics, information forensics, and security have improved the recognition effectiveness of biometric systems. However, an ever-growing challenge is the vulnerability of such systems against presentation attacks, in which impostor users create synthetic samples from the original biometric information of a legitimate user and show them to the acquisition sensor seeking to authenticate themselves as legitimate users. Depending on the trait used by the biometric authentication, the attack types vary with the type of material used to build the synthetic samples. For instance, in facial biometric systems, an attempted attack is characterized by the type of material the impostor uses such as a photograph, a digital video, or a 3D mask with the facial information of a target user. In iris-based biometrics, presentation attacks can be accomplished with printout photographs or with contact lenses containing the iris patterns of a target user or even synthetic texture patterns. In fingerprint biometric systems, impostor users can deceive the authentication process using replicas of the fingerprint patterns built with synthetic materials such as latex, play-doh, silicone, among others. This research aimed at developing presentation attack detection (PAD) solutions whose objective is to detect attempted attacks considering different attack types, in each modality. The lines of investigation presented in this thesis aimed at devising and developing representations based on spatial, temporal and spectral information from noise signature, intrinsic properties of the biometric data (e.g., albedo, reflectance, and depth maps), and supervised feature learning techniques, taking into account different testing scenarios including cross-sensor, intra-, and inter-dataset scenarios. The main findings and contributions presented in this thesis include: the creation of a large and publicly available benchmark containing 17K videos of presentation attacks and bona-fide presentations simulations in a facial biometric system, whose collect were formally authorized by the Research Ethics Committee at Unicamp; the development of novel approaches to modeling and analysis of extrinsic properties of biometric samples related to artifacts added during the manufacturing of the synthetic samples and their capture by the acquisition sensor, whose results were superior to several approaches published in the literature that use traditional methods for image analysis (e.g., texture-based analysis); the investigation of an approach based on the analysis of intrinsic properties of faces, estimated from the information of shadows present on their surface; and the investigation of different approaches to automatically learning representations related to our problem, whose results were superior or competitive to state-of-the-art methods for the biometric modalities considered in this thesis. We also considered in this research the design of efficient neural networks with shallow architectures capable of learning characteristics related to our problem from small sets of data available to develop and evaluate PAD solutionsDoutoradoCiĂŞncia da ComputaçãoDoutor em CiĂŞncia da Computação140069/2016-0
CNPq, 142110/2017-5CAPESCNP
Handbook of Vascular Biometrics
This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers
Textural features for fingerprint liveness detection
The main topic ofmy research during these three years concerned biometrics and in particular
the Fingerprint Liveness Detection (FLD), namely the recognition of fake fingerprints.
Fingerprints spoofing is a topical issue as evidenced by the release of the latest iPhone and
Samsung Galaxy models with an embedded fingerprint reader as an alternative to passwords.
Several videos posted on YouTube show how to violate these devices by using fake
fingerprints which demonstrated how the problemof vulnerability to spoofing constitutes a
threat to the existing fingerprint recognition systems.
Despite the fact that many algorithms have been proposed so far, none of them showed
the ability to clearly discriminate between real and fake fingertips. In my work, after a study
of the state-of-the-art I paid a special attention on the so called textural algorithms. I first
used the LBP (Local Binary Pattern) algorithm and then I worked on the introduction of the
LPQ (Local Phase Quantization) and the BSIF (Binarized Statistical Image Features) algorithms
in the FLD field.
In the last two years I worked especially on what we called the “user specific” problem.
In the extracted features we noticed the presence of characteristic related not only to the
liveness but also to the different users. We have been able to improve the obtained results
identifying and removing, at least partially, this user specific characteristic.
Since 2009 the Department of Electrical and Electronic Engineering of the University of
Cagliari and theDepartment of Electrical and Computer Engineering of the ClarksonUniversity
have organized the Fingerprint Liveness Detection Competition (LivDet). I have been
involved in the organization of both second and third editions of the Fingerprint Liveness
Detection Competition (LivDet 2011 and LivDet 2013) and I am currently involved in the acquisition
of live and fake fingerprint that will be inserted in three of the LivDet 2015 datasets