MobiBits: Multimodal Mobile Biometric Database

This paper presents a novel database comprising representations of five different biometric characteristics, collected in a mobile, unconstrained or semi-constrained setting with three different mobile devices, including characteristics previously unavailable in existing datasets, namely hand images, thermal hand images, and thermal face images, all acquired with a mobile, off-the-shelf device. In addition to this collection of data we perform an extensive set of experiments providing insight on benchmark recognition performance that can be achieved with these data, carried out with existing commercial and academic biometric solutions. This is the first known to us mobile biometric database introducing samples of biometric traits such as thermal hand images and thermal face images. We hope that this contribution will make a valuable addition to the already existing databases and enable new experiments and studies in the field of mobile authentication. The MobiBits database is made publicly available to the research community at no cost for non-commercial purposes.Comment: Submitted for the BIOSIG2018 conference on June 18, 2018. Accepted for publication on July 20, 201

Cross-spectral Iris Recognition for Mobile Applications using High-quality Color Images, Journal of Telecommunications and Information Technology, 2016, nr 3

With the recent shift towards mobile computing, new challenges for biometric authentication appear on the horizon. This paper provides a comprehensive study of cross-spectral iris recognition in a scenario, in which high quality color images obtained with a mobile phone are used against enrollment images collected in typical, near-infrared setups. Grayscale conversion of the color images that employs selective RGB channel choice depending on the iris coloration is shown to improve the recognition accuracy for some combinations of eye colors and matching software, when compared to using the red channel only, with equal error rates driven down to as low as 2%. The authors are not aware of any other paper focusing on cross-spectral iris recognition is a scenario with near-infrared enrollment using a professional iris recognition setup and then a mobile-based veri cation employing color images

Features for Cross Spectral Image Matching: A Survey

In recent years, cross spectral matching has been gaining attention in various biometric systems for identification and verification purposes. Cross spectral matching allows images taken under different electromagnetic spectrums to match each other. In cross spectral matching, one of the keys for successful matching is determined by the features used for representing an image. Therefore, the feature extraction step becomes an essential task. Researchers have improved matching accuracy by developing robust features. This paper presents most commonly selected features used in cross spectral matching. This survey covers basic concepts of cross spectral matching, visual and thermal features extraction, and state of the art descriptors. In the end, this paper provides a description of better feature selection methods in cross spectral matching

Análise de propriedades intrínsecas e extrínsecas de amostras biométricas para detecção de ataques de apresentação

Orientadores: Anderson de Rezende Rocha, Hélio 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 biométricos. No entanto, um desafio ainda em aberto é a vulnerabilidade de tais sistemas contra ataques de apresentação, nos quais os usuários impostores criam amostras sintéticas, a partir das informações biométricas originais de um usuário legítimo, e as apresentam ao sensor de aquisição procurando se autenticar como um usuário válido. Dependendo da modalidade biométrica, os tipos de ataque variam de acordo com o tipo de material usado para construir as amostras sintéticas. Por exemplo, em biometria facial, uma tentativa de ataque é caracterizada quando um usuário impostor apresenta ao sensor de aquisição uma fotografia, um vídeo digital ou uma máscara 3D com as informações faciais de um usuário-alvo. Em sistemas de biometria baseados em íris, os ataques de apresentação podem ser realizados com fotografias impressas ou com lentes de contato contendo os padrões de íris de um usuário-alvo ou mesmo padrões de textura sintéticas. Nos sistemas biométricos de impressão digital, os usuários impostores podem enganar o sensor biométrico usando réplicas dos padrões de impressão digital construídas com materiais sintéticos, 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 biométricos faciais, de íris e de impressão digital. As linhas de investigação apresentadas nesta tese incluem o desenvolvimento de representações baseadas nas informações espaciais, temporais e espectrais da assinatura de ruído; em propriedades intrínsecas das amostras biométricas (e.g., mapas de albedo, de reflectância e de profundidade) e em técnicas de aprendizagem supervisionada de características. Os principais resultados e contribuições apresentadas nesta tese incluem: a criação de um grande conjunto de dados publicamente disponível contendo aproximadamente 17K videos de simulações de ataques de apresentações e de acessos genuínos em um sistema biométrico facial, os quais foram coletados com a autorização do Comitê de Ética em Pesquisa da Unicamp; o desenvolvimento de novas abordagens para modelagem e análise de propriedades extrínsecas das amostras biométricas relacionadas aos artefatos que são adicionados durante a fabricação das amostras sintéticas e sua captura pelo sensor de aquisição, cujos resultados de desempenho foram superiores a diversos métodos propostos na literature que se utilizam de métodos tradicionais de análise de images (e.g., análise de textura); a investigação de uma abordagem baseada na análise de propriedades intrínsecas das faces, estimadas a partir da informação de sombras presentes em sua superfície; e, por fim, a investigação de diferentes abordagens baseadas em redes neurais convolucionais para o aprendizado automático de características relacionadas ao nosso problema, cujos resultados foram superiores ou competitivos aos métodos considerados estado da arte para as diferentes modalidades biométricas consideradas nesta tese. A pesquisa também considerou o projeto de eficientes redes neurais com arquiteturas rasas capazes de aprender características relacionadas ao nosso problema a partir de pequenos conjuntos de dados disponíveis 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 solutionsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação140069/2016-0 CNPq, 142110/2017-5CAPESCNP

UFPR-Periocular: A Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios

Recently, ocular biometrics in unconstrained environments using images obtained at visible wavelength have gained the researchers' attention, especially with images captured by mobile devices. Periocular recognition has been demonstrated to be an alternative when the iris trait is not available due to occlusions or low image resolution. However, the periocular trait does not have the high uniqueness presented in the iris trait. Thus, the use of datasets containing many subjects is essential to assess biometric systems' capacity to extract discriminating information from the periocular region. Also, to address the within-class variability caused by lighting and attributes in the periocular region, it is of paramount importance to use datasets with images of the same subject captured in distinct sessions. As the datasets available in the literature do not present all these factors, in this work, we present a new periocular dataset containing samples from 1,122 subjects, acquired in 3 sessions by 196 different mobile devices. The images were captured under unconstrained environments with just a single instruction to the participants: to place their eyes on a region of interest. We also performed an extensive benchmark with several Convolutional Neural Network (CNN) architectures and models that have been employed in state-of-the-art approaches based on Multi-class Classification, Multitask Learning, Pairwise Filters Network, and Siamese Network. The results achieved in the closed- and open-world protocol, considering the identification and verification tasks, show that this area still needs research and development

Periocular Biometrics: A Modality for Unconstrained Scenarios

Periocular refers to the region of the face that surrounds the eye socket. This is a feature-rich area that can be used by itself to determine the identity of an individual. It is especially useful when the iris or the face cannot be reliably acquired. This can be the case of unconstrained or uncooperative scenarios, where the face may appear partially occluded, or the subject-to-camera distance may be high. However, it has received revived attention during the pandemic due to masked faces, leaving the ocular region as the only visible facial area, even in controlled scenarios. This paper discusses the state-of-the-art of periocular biometrics, giving an overall framework of its most significant research aspects