A framework for improving the performance of verification algorithms with a low false positive rate requirement and limited training data

In this paper we address the problem of matching patterns in the so-called verification setting in which a novel, query pattern is verified against a single training pattern: the decision sought is whether the two match (i.e. belong to the same class) or not. Unlike previous work which has universally focused on the development of more discriminative distance functions between patterns, here we consider the equally important and pervasive task of selecting a distance threshold which fits a particular operational requirement - specifically, the target false positive rate (FPR). First, we argue on theoretical grounds that a data-driven approach is inherently ill-conditioned when the desired FPR is low, because by the very nature of the challenge only a small portion of training data affects or is affected by the desired threshold. This leads us to propose a general, statistical model-based method instead. Our approach is based on the interpretation of an inter-pattern distance as implicitly defining a pattern embedding which approximately distributes patterns according to an isotropic multi-variate normal distribution in some space. This interpretation is then used to show that the distribution of training inter-pattern distances is the non-central chi2 distribution, differently parameterized for each class. Thus, to make the class-specific threshold choice we propose a novel analysis-by-synthesis iterative algorithm which estimates the three free parameters of the model (for each class) using task-specific constraints. The validity of the premises of our work and the effectiveness of the proposed method are demonstrated by applying the method to the task of set-based face verification on a large database of pseudo-random head motion videos.Comment: IEEE/IAPR International Joint Conference on Biometrics, 201

Setting a world record in 3D face recognition

Biometrics - recognition of persons based on how they look or behave, is the main subject of research at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente. Examples are finger print recognition, iris and face recognition. A relatively new field is 3D face recognition based on the shape of the face rather that its appearance. This paper presents a method for 3D face recognition developed at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente and published in 2011. The paper also shows that noteworthy performance gains can be obtained by optimisation of an existing method. The method is based on registration to an intrinsic coordinate system using the vertical symmetry plane of the head, the tip of the nose and the slope of the nose bridge. For feature extraction and classification multiple regional PCA-LDA-likelihood ratio based classifiers are fused using a fixed FAR voting strategy. We present solutions for correction of motion artifacts in 3D scans, improved registration and improved training of the used PCA-LDA classifier using automatic outlier removal. These result in a notable improvement of the recognition rates. The all vs all verification rate for the FRGC v2 dataset jumps to 99.3% and the identification rate for the all vs first to 99.4%. Both are to our knowledge the best results ever obtained for these benchmarks by a fairly large margin

Face Image Retrieval in Image Processing – A Survey

The task of face recognition has been actively researched in recent years. Face recognition has been a challenging and interesting area in real time applications. With the exponentially growing images, large-scale content-based face image retrieval is an enabling technology for many emerging applications. A large number of face recognition algorithms have been developed in last decades. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. Here first we present an overview of face recognition and discuss the methodology and its functioning. Thereafter we represent the most recent face recognition techniques listing their advantages and disadvantages. Some techniques specified here also improve the efficiency of face recognition under various illumination and expression condition of face images This include PCA, LDA, SVM, Gabor wavelet soft computing tool like ANN for recognition and various hybrid combination of these techniques. This review investigates all these methods with parameters that challenges face recognition like illumination, pose variation, facial expressions. This paper also focuses on related work done in the area of face image retrieval

Dense 3D Face Correspondence

We present an algorithm that automatically establishes dense correspondences between a large number of 3D faces. Starting from automatically detected sparse correspondences on the outer boundary of 3D faces, the algorithm triangulates existing correspondences and expands them iteratively by matching points of distinctive surface curvature along the triangle edges. After exhausting keypoint matches, further correspondences are established by generating evenly distributed points within triangles by evolving level set geodesic curves from the centroids of large triangles. A deformable model (K3DM) is constructed from the dense corresponded faces and an algorithm is proposed for morphing the K3DM to fit unseen faces. This algorithm iterates between rigid alignment of an unseen face followed by regularized morphing of the deformable model. We have extensively evaluated the proposed algorithms on synthetic data and real 3D faces from the FRGCv2, Bosphorus, BU3DFE and UND Ear databases using quantitative and qualitative benchmarks. Our algorithm achieved dense correspondences with a mean localisation error of 1.28mm on synthetic faces and detected $14$ anthropometric landmarks on unseen real faces from the FRGCv2 database with 3mm precision. Furthermore, our deformable model fitting algorithm achieved 98.5% face recognition accuracy on the FRGCv2 and 98.6% on Bosphorus database. Our dense model is also able to generalize to unseen datasets.Comment: 24 Pages, 12 Figures, 6 Tables and 3 Algorithm

Face Recognition: Issues, Methods and Alternative Applications

Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention. It is due to availability of feasible technologies, including mobile solutions. Research in automatic face recognition has been conducted since the 1960s, but the problem is still largely unsolved. Last decade has provided significant progress in this area owing to advances in face modelling and analysis techniques. Although systems have been developed for face detection and tracking, reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers. There are several reasons for recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world, face analysis and modelling techniques in multimedia data management and computer entertainment. In this chapter, we have discussed face recognition processing, including major components such as face detection, tracking, alignment and feature extraction, and it points out the technical challenges of building a face recognition system. We focus on the importance of the most successful solutions available so far. The final part of the chapter describes chosen face recognition methods and applications and their potential use in areas not related to face recognition

Deformation Based 3D Facial Expression Representation

We propose a deformation based representation for analyzing expressions from 3D faces. A point cloud of a 3D face is decomposed into an ordered deformable set of curves that start from a fixed point. Subsequently, a mapping function is defined to identify the set of curves with an element of a high dimensional matrix Lie group, specifically the direct product of SE(3). Representing 3D faces as an element of a high dimensional Lie group has two main advantages. First, using the group structure, facial expressions can be decoupled from a neutral face. Second, an underlying non-linear facial expression manifold can be captured with the Lie group and mapped to a linear space, Lie algebra of the group. This opens up the possibility of classifying facial expressions with linear models without compromising the underlying manifold. Alternatively, linear combinations of linearised facial expressions can be mapped back from the Lie algebra to the Lie group. The approach is tested on the BU-3DFE and the Bosphorus datasets. The results show that the proposed approach performed comparably, on the BU-3DFE dataset, without using features or extensive landmark points

Computational Modeling of Facial Response for Detecting Differential Traits in Autism Spectrum Disorders

This dissertation proposes novel computational modeling and computer vision methods for the analysis and discovery of differential traits in subjects with Autism Spectrum Disorders (ASD) using video and three-dimensional (3D) images of face and facial expressions. ASD is a neurodevelopmental disorder that impairs an individual’s nonverbal communication skills. This work studies ASD from the pathophysiology of facial expressions which may manifest atypical responses in the face. State-of-the-art psychophysical studies mostly employ na¨ıve human raters to visually score atypical facial responses of individuals with ASD, which may be subjective, tedious, and error prone. A few quantitative studies use intrusive sensors on the face of the subjects with ASD, which in turn, may inhibit or bias the natural facial responses of these subjects. This dissertation proposes non-intrusive computer vision methods to alleviate these limitations in the investigation for differential traits from the spontaneous facial responses of individuals with ASD. Two IRB-approved psychophysical studies are performed involving two groups of age-matched subjects: one for subjects diagnosed with ASD and the other for subjects who are typically-developing (TD). The facial responses of the subjects are computed from their facial images using the proposed computational models and then statistically analyzed to infer about the differential traits for the group with ASD. A novel computational model is proposed to represent the large volume of 3D facial data in a small pose-invariant Frenet frame-based feature space. The inherent pose-invariant property of the proposed features alleviates the need for an expensive 3D face registration in the pre-processing step. The proposed modeling framework is not only computationally efficient but also offers competitive performance in 3D face and facial expression recognition tasks when compared with that of the state-ofthe-art methods. This computational model is applied in the first experiment to quantify subtle facial muscle response from the geometry of 3D facial data. Results show a statistically significant asymmetry in specific pair of facial muscle activation (p\u3c0.05) for the group with ASD, which suggests the presence of a psychophysical trait (also known as an ’oddity’) in the facial expressions. For the first time in the ASD literature, the facial action coding system (FACS) is employed to classify the spontaneous facial responses based on facial action units (FAUs). Statistical analyses reveal significantly (p\u3c0.01) higher prevalence of smile expression (FAU 12) for the ASD group when compared with the TD group. The high prevalence of smile has co-occurred with significantly averted gaze (p\u3c0.05) in the group with ASD, which is indicative of an impaired reciprocal communication. The metric associated with incongruent facial and visual responses suggests a behavioral biomarker for ASD. The second experiment shows a higher prevalence of mouth frown (FAU 15) and significantly lower correlations between the activation of several FAU pairs (p\u3c0.05) in the group with ASD when compared with the TD group. The proposed computational modeling in this dissertation offers promising biomarkers, which may aid in early detection of subtle ASD-related traits, and thus enable an effective intervention strategy in the future

Projeto de um descritor para o alinhamento de imagens de profundidade de superfícies com aplicação em visão robótica

Tese (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2014.O processo de Reconstrução 3D de objetos a partir de imagens de profundidade compreende três fases: a) aquisição, na qual a superfície do objeto é digitalizada a partir de vários pontos de vista, gerando um conjunto de imagens de profundidade; b) registro, no qual uma transformação rígida M deve ser estimada para posicionar as imagens, par a par, em um único referencial; e c) combinação ou “matching”, na qual redundâncias entre vistas registradas são tratadas para construir um modelo 3D completo único. Na etapa de registro distinguem-se ainda duas etapas: pré-alinhamento e alinhamento fino. Na primeira, uma aproximação inicial de registro é estimada através da extração de pontos característicos correspondentes de duas imagens que ocupem a mesma posição no espaço do objeto e do cálculo de uma transformação rígida de forma que a distância quadrática entre os pares de pontos característicos correspondentes seja minimizada. Na segunda etapa, iterações são aplicadas sobre a transformação obtida para que os erros de alinhamento sejam minimizados. A fase de alinhamento fino é um problema resolvido, muito diferente da etapa de pré-alinhamento, cujo problema é o desconhecimento da correspondência entre as regiões de sobreposição; por tal motivo, a solução mais usual é escolher pontos que tenham alguma característica que os distingam do resto. Nesta tese propôs-se uma metodologia que está baseada na segmentação/reconstrução de imagens de profundidade fazendo uso da técnica de detecção de bordas aliada à técnica de agrupamento usando dizimação de malhas. A partir das bordas propõe-se um descritor de propriedades invariantes sob transformações de similaridade que incluem transformações de rotação, translação e escala uniforme como também robusto a ruído. Através do descritor proposto, são extraídos pontos correspondentes de ambas as imagens, e gera-se como valor de saída uma assinatura que se relaciona diretamente ao ponto considerando a disposição geométrica de sua vizinhança. A assinatura resultante atribui um rótulo ao ponto na imagem de profundidade, o que permite que, no processo de busca, seja aumentada a eficiência de correspondências, facilitando a identificação de possíveis zonas de sobreposição e reduzindo a ambiguidade no processo de busca. Odescritor foi avaliado com diferentes imagens e seus resultados foram comparados com os de outros autores com as mesmas imagens. Para verificar a validade das transformações candidatas, cada uma delas passa por um alinhamento fino, em que se avalia os erros de ajuste entre as duas imagens de profundidade através do algoritmo ICP (iterative closest point). A transformação que alinha o maior número de pontos é considerada a solução. Destaca-se que a principal contribuição deste trabalho é o desenvolvimento de uma técnica de pré-alinhamento e de sua integração com uma técnica de alinhamento fino, definindo de forma geral uma metodologia completa para registro e reconstrução de modelos tridimensionais de superfícies que tenham variação de curvatura suave em uma vizinhança, a partir de curvas de contorno 3D de variações geométricas nesta superfície. _______________________________________________________________________________ ABSTRACTThe process of reconstruction of 3D objects from range images consists of three steps: a) acquisition, in which the surface of the object is scanned from various points of view, generating n depth images; b) registration, in which a rigid transformation M must be estimated to locate images, pair by pair, in a single framework; c) matching, in which redundancies between registered views are processed to construct a single complete 3D model. Registration is further distinguished in two stages: pre-alignment and fine alignment. First, an initial approximation of the register is estimated by extracting the corresponding feature points from the two images that occupy the same position in the object space and the computation of a rigid transformation M so that the squared distance between pairs of corresponding feature points is minimized. In the second step, iterations are applied to the transformation obtained for alignment errors to be minimized. The fine alignment stage is a solved problem, very differently from the pre-alignment step, whose problem is the lack of correspondence between the overlap regions; therefore, the most common solution is to choose points that have some characteristics that distinguish them from the rest. This thesis proposes a methodology that is based on segmentation / reconstruction of depth images making use of an edge detection technique combined with a clustering technique using mesh decimation edges. From the edges it is proposed a descriptor which is invariant to similarity transformations including rotation, translation and uniform scale as also robust to noise. Through the proposed descriptor, n corresponding points from the two images are extracted, and a signature value is generated as output that is related directly to the point considering the geometrical distribution of its neighborhood. The resulting signature assigns a label to the point in the depth image, which allows that in the search process the efficiency of the correspondences is increased, facilitating the identification of possible areas of overlapping and reducing the ambiguity in the search process. The descriptor was evaluated with different images and their results were compared with those of other authors that used the same images. To verify the validity of the candidate transformations, each of which passes through a fine alignment, in which the fitting errors are evaluated between the two depth images by the ICP (iterative closest point) algorithm. The transformation that aligns the largest number of points is considered the solution. It is noteworthy that the main contribution of this work is the development of a technique for pre-alignment and its integration with a fine alignment technique, defining a complete methodology for registration and reconstruction of three-dimensional surface models, with smooth curvature variation in a neighborhood, from the curves of 3D contours. _______________________________________________________________________________ RESUMENEl proceso de Reconstrucción 3D de objetos a partir de imágenes de profundidad comprende tres fases: a) adquisición, en la cual la superficie del objeto es digitalizada a partir de varios puntos de vista, generando un conjunto de imágenes de profundidad. b) registro, en la cual una transformación rígida M debe ser estimada para posicionar las imágenes, par a par, en un único marco referencial. c) combinación o “matching”, en la cual redundancias entre vistas registradas son tratadas para construir un modelo 3D completo único. En la etapa de registro se distinguen dos etapas: pre-alineamiento y alineamiento fino. En la primera, una aproximación inicial de registro es estimada a través da extracción de puntos característicos correspondientes en las dos imágenes, que ocupen la misma posición en el espacio del objeto y el cálculo de una transformación rígida M de forma que la distancia cuadrada entre los pares de puntos característicos correspondientes sea minimizada. En la segunda etapa, iteraciones son aplicadas sobre la transformación obtenida para que los errores de alineamiento sean minimizados. La fase de alineamiento fino es un problema resuelto, muy diferente a la etapa de pre-alineamiento, cuyo problema es el desconocimiento de la correspondencia entre las regiones de sobre posición; por tal motivo, la solución más usual es escoger puntos que tengan alguna característica que los distingan del resto. La fase de alineamiento fino es un problema resuelto, muy diferente a la etapa de pre-alineamiento, cuyo problema es el desconocimiento de la correspondencia entre las regiones de sobre posición; por tal motivo, la solución más usual es escoger puntos que tengan alguna característica que los distingan del resto. La propuesta deste trabajo es contribuir en la búsqueda de una solución más eficaz, tratando de superar inconvenientes que hacen que otros métodos fallen. En esta tesis se plantea una metodología que está basada en la segmentación/reconstrucción de imágenes de profundidad, utilizando la técnica de detección de bordas aliada a la técnica de agrupamiento de mallas. A partir de las bordas se propuso un descriptor de propiedades invariantes sobre transformaciones de semejanza que incluye transformaciones de rotación, translación y escala uniforme como también robusto al ruido. A través del descriptor propuesto, son extraídos puntos correspondientes de las dos imágenes, generando como valor de salida un código que lo relaciona directamente al punto considerando la disposición geométrica de su vecindad. El código resultante atribuye una etiqueta al punto de la imagen de profundidad, lo que permite que en el proceso de búsqueda, sea aumentada la eficiencia de correspondencias, facilitando la identificación de posibles zonas de superposición y reduciendo la ambigüedad en el proceso de búsqueda. El descriptor fue evaluado con diferentes imágenes y sus resultados fueron comparados con los resultados de otros autores usando las mismas imágenes. Para verificar la validad de las transformaciones candidatas, cada una de ellas pasó por un alineamiento fino, en que se evalúa los errores de ajuste entre las dos imágenes de profundidad a través del algoritmo ICP (iterative closest point). La transformación que alinee el mayor número de puntos es considerada la solución. Se destaca que la principal contribución deste trabajo es el desenvolvimiento de una técnica de pre-alineamiento y su integración con una técnica de alineamiento fino, definiendo de forma general una metodología completa para el registro y reconstrucción de modelos tridimensionales de superficies, con variación de curvatura suave en una vecindad, a partir de curvas de contornos 3D de variaciones geométricas en esta superfície