Biometric recognition based on the texture along palmprint lines

Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

Performance, Development, and Analysis of Tactile vs. Visual Receptive Fields in Texture Tasks

Texture segmentation is an effortless process in scene analysis, yet its neural mechanisms are not sufficiently understood. A common assumption in most current approaches is that texture segmentation is a vision problem. However, considering that texture is basically a surface property, this assumption can at times be misleading. One interesting possibility is that texture may be more intimately related with touch than with vision. Recent neurophysiological findings showed that receptive fields (RFs) for touch resemble that of vision, albeit with some subtle differences. To leverage on this, here I propose three ways to investigate the tactile receptive fields in the context of texture processing: (1) performance, (2) development, and (3) analysis. For performance, I tested how such distinct properties in tactile receptive fields can affect texture segmentation performance, as compared to that of visual receptive fields. Preliminary results suggest that touch has an advantage over vision in texture segmentation. These results support the idea that texture is fundamentally a tactile (surface) property. The next question is what drives the two types of RFs, visual and tactile, to become different during cortical development? I investigated the possibility that tactile RF and visual RF emerge based on the same cortical learning process, where the only difference is in the input type, natural-scene-like vs. texture-like. The main result is that RFs trained on natural scenes develop RFs resembling visual RFs, while those trained on texture resemble tactile RFs. These results again suggest a tight link between texture and the tactile modality, from a developmental context. To investigate further the functional properties of these RFs in texture processing, the response of tactile RFs and visual RFs were analyzed with manifold learning and with statistical approaches. The results showed that touch-based manifold seems more suitable for texture processing and desirable properties found in visual RF response can carry over to those in the tactile domain. These results are expected to shed new light on the role of tactile perception of texture; help develop more powerful, biologically inspired texture segmentation algorithms; and further clarify the differences and similarities between touch and vision

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

Improving less constrained iris recognition

The iris has been one of the most reliable biometric traits for automatic human authentication due to its highly stable and distinctive patterns. Traditional iris recognition algorithms have achieved remarkable performance in strictly constrained environments, with the subject standing still and with the iris captured at a close distance. This enables the wide deployment of iris recognition systems in applications such as border control and access control. However, in less constrained environments with the subject at-a-distance and on-the-move, the iris recognition performance is significantly deteriorated, since such environments induce noise and degradations in iris captures. This restricts the applicability and practicality of iris recognition technology for some real-world applications with more open capturing conditions, such as surveillance, forensic and mobile device security applications. Therefore, robust algorithms for less constrained iris recognition are desirable for the wider deployment of iris recognition systems. This thesis focuses on improving less constrained iris recognition. Five methods are proposed to improve the performance of different stages in less constrained iris recognition. First, a robust iris segmentation algorithm is developed using l1-norm regression and model selection. This algorithm formulates iris segmentation as robust l1-norm regression problems. To further enhance the robustness, multiple segmentation results are produced by applying l1-norm regression to different models, and a model selection technique is used to select the most reliable result. Second, an iris liveness detection method using regional features is investigated. This method seeks not only low level features, but also high level feature distributions for more accurate and robust iris liveness detection. Third, a signal-level information fusion algorithm is presented to mitigate the noise in less constrained iris captures. With multiple noisy iris captures, this algorithm proposes a sparse-error low rank matrix factorization model to separate noiseless iris structures and noise. The noiseless structures are preserved and emphasised during the fusion process, while the noise is suppressed, in order to obtain more reliable signals for recognition. Fourth, a method to generate optimal iris codes is proposed. This method considers iris code generation from the perspective of optimization. It formulates traditional iris code generation method as an optimization problem; an additional objective term modelling the spatial correlations in iris codes is applied to this optimization problem to produce more effective iris codes. Fifth, an iris weight map method is studied for robust iris matching. This method considers both intra-class bit stability and inter-class bit discriminability in iris codes. It emphasises highly stable and discriminative bits for iris matching, enhancing the robustness of iris matching. Comprehensive experimental analysis are performed on benchmark datasets for each of the above methods. The results indicate that the presented methods are effective for less constrained iris recognition, generally improving state-of-the-art performance

Skin texture features for face recognition

Face recognition has been deployed in a wide range of important applications including surveillance and forensic identification. However, it still seems to be a challenging problem as its performance severely degrades under illumination, pose and expression variations, as well as with occlusions, and aging. In this thesis, we have investigated the use of local facial skin data as a source of biometric information to improve human recognition. Skin texture features have been exploited in three major tasks, which include (i) improving the performance of conventional face recognition systems, (ii) building an adaptive skin-based face recognition system, and (iii) dealing with circumstances when a full view of the face may not be avai'lable. Additionally, a fully automated scheme is presented for localizing eyes and mouth and segmenting four facial regions: forehead, right cheek, left cheek and chin. These four regions are divided into nonoverlapping patches with equal size. A novel skin/non-skin classifier is proposed for detecting patches containing only skin texture and therefore detecting the pure-skin regions. Experiments using the XM2VTS database indicate that the forehead region has the most significant biometric information. The use of forehead texture features improves the rank-l identification of Eigenfaces system from 77.63% to 84.07%. The rank-l identification is equal 93.56% when this region is fused with Kernel Direct Discriminant Analysis algorithm

Perception of Human Movement Based on Modular Movement Primitives

People can identify and understand human movement from very degraded visual information without effort. A few dots representing the position of the joints are enough to induce a vivid and stable percept of the underlying movement. Due to this ability, the realistic animation of 3D characters requires great skill. Studying the constituents of movement that looks natural would not only help these artists, but also bring better understanding of the underlying information processing in the brain. Analogous to the hurdles in animation, the efforts of roboticists reflect the complexity of motion production: controlling the many degrees of freedom of a body requires time-consuming computations. Modularity is one strategy to address this problem: Complex movement can be decomposed into simple primitives. A few primitives can conversely be used to compose a large number of movements. Many types of movement primitives (MPs) have been proposed on different levels of information processing hierarchy in the brain. MPs have mostly been proposed for movement production. Yet, modularity based on primitives might similarly enable robust movement perception. For my thesis, I have conducted perceptual experiments based on the assumption of a shared representation of perception and action based on MPs. The three different types of MPs I have investigated are temporal MPs (TMP), dynamical MPs (DMP), and coupled Gaussian process dynamical models (cGPDM). The MP-models have been trained on natural movements to generate new movements. I then perceptually validated these artificial movements in different psychophysical experiments. In all experiments I used a two-alternative forced choice paradigm, in which human observers were presented a movement based on motion-capturing data, and one generated by an MP-model. They were then asked to chose the movement which they perceived as more natural. In the first experiment I investigated walking movements, and found that, in line with previous results, faithful representation of movement dynamics is more important than good reconstruction of pose. In the second experiment I investigated the role of prediction in perception using reaching movements. Here, I found that perceived naturalness of the predictions is similar to the perceived naturalness of movements itself obtained in the first experiment. I have found that MP models are able to produce movement that looks natural, with the TMP achieving the highest perceptual scores as well highest predictiveness of perceived naturalness among the three model classes, suggesting their suitability for a shared representation of perception and action