A survey of face recognition techniques under occlusion

The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. In this paper, we restrict the scope to occluded face recognition. First, we explore what the occlusion problem is and what inherent difficulties can arise. As a part of this review, we introduce face detection under occlusion, a preliminary step in face recognition. Second, we present how existing face recognition methods cope with the occlusion problem and classify them into three categories, which are 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, we analyze the motivations, innovations, pros and cons, and the performance of representative approaches for comparison. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed

Subspace Representations for Robust Face and Facial Expression Recognition

Analyzing human faces and modeling their variations have always been of interest to the computer vision community. Face analysis based on 2D intensity images is a challenging problem, complicated by variations in pose, lighting, blur, and non-rigid facial deformations due to facial expressions. Among the different sources of variation, facial expressions are of interest as important channels of non-verbal communication. Facial expression analysis is also affected by changes in view-point and inter-subject variations in performing different expressions. This dissertation makes an attempt to address some of the challenges involved in developing robust algorithms for face and facial expression recognition by exploiting the idea of proper subspace representations for data. Variations in the visual appearance of an object mostly arise due to changes in illumination and pose. So we first present a video-based sequential algorithm for estimating the face albedo as an illumination-insensitive signature for face recognition. We show that by knowing/estimating the pose of the face at each frame of a sequence, the albedo can be efficiently estimated using a Kalman filter. Then we extend this to the case of unknown pose by simultaneously tracking the pose as well as updating the albedo through an efficient Bayesian inference method performed using a Rao-Blackwellized particle filter. Since understanding the effects of blur, especially motion blur, is an important problem in unconstrained visual analysis, we then propose a blur-robust recognition algorithm for faces with spatially varying blur. We model a blurred face as a weighted average of geometrically transformed instances of its clean face. We then build a matrix, for each gallery face, whose column space spans the space of all the motion blurred images obtained from the clean face. This matrix representation is then used to define a proper objective function and perform blur-robust face recognition. To develop robust and generalizable models for expression analysis one needs to break the dependence of the models on the choice of the coordinate frame of the camera. To this end, we build models for expressions on the affine shape-space (Grassmann manifold), as an approximation to the projective shape-space, by using a Riemannian interpretation of deformations that facial expressions cause on different parts of the face. This representation enables us to perform various expression analysis and recognition algorithms without the need for pose normalization as a preprocessing step. There is a large degree of inter-subject variations in performing various expressions. This poses an important challenge on developing robust facial expression recognition algorithms. To address this challenge, we propose a dictionary-based approach for facial expression analysis by decomposing expressions in terms of action units (AUs). First, we construct an AU-dictionary using domain experts' knowledge of AUs. To incorporate the high-level knowledge regarding expression decomposition and AUs, we then perform structure-preserving sparse coding by imposing two layers of grouping over AU-dictionary atoms as well as over the test image matrix columns. We use the computed sparse code matrix for each expressive face to perform expression decomposition and recognition. Most of the existing methods for the recognition of faces and expressions consider either the expression-invariant face recognition problem or the identity-independent facial expression recognition problem. We propose joint face and facial expression recognition using a dictionary-based component separation algorithm (DCS). In this approach, the given expressive face is viewed as a superposition of a neutral face component with a facial expression component, which is sparse with respect to the whole image. This assumption leads to a dictionary-based component separation algorithm, which benefits from the idea of sparsity and morphological diversity. The DCS algorithm uses the data-driven dictionaries to decompose an expressive test face into its constituent components. The sparse codes we obtain as a result of this decomposition are then used for joint face and expression recognition

Robust single-sample face recognition by sparsity-driven sub-dictionary learning using deep features

Face recognition using a single reference image per subject is challenging, above all when referring to a large gallery of subjects. Furthermore, the problem hardness seriously increases when the images are acquired in unconstrained conditions. In this paper we address the challenging Single Sample Per Person (SSPP) problem considering large datasets of images acquired in the wild, thus possibly featuring illumination, pose, face expression, partial occlusions, and low-resolution hurdles. The proposed technique alternates a sparse dictionary learning technique based on the method of optimal direction and the iterative \u2113 0 -norm minimization algorithm called k-LIMAPS. It works on robust deep-learned features, provided that the image variability is extended by standard augmentation techniques. Experiments show the effectiveness of our method against the hardness introduced above: first, we report extensive experiments on the unconstrained LFW dataset when referring to large galleries up to 1680 subjects; second, we present experiments on very low-resolution test images up to 8 7 8 pixels; third, tests on the AR dataset are analyzed against specific disguises such as partial occlusions, facial expressions, and illumination problems. In all the three scenarios our method outperforms the state-of-the-art approaches adopting similar configurations

A survey of face recognition techniques under occlusion

The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. In this paper, we restrict the scope to occluded face recognition. First, we explore what the occlusion problem is and what inherent difficulties can arise. As a part of this review, we introduce face detection under occlusion, a preliminary step in face recognition. Second, we present how existing face recognition methods cope with the occlusion problem and classify them into three categories, which are 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, we analyze the motivations, innovations, pros and cons, and the performance of representative approaches for comparison. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed

DICTIONARIES AND MANIFOLDS FOR FACE RECOGNITION ACROSS ILLUMINATION, AGING AND QUANTIZATION

During the past many decades, many face recognition algorithms have been proposed. The face recognition problem under controlled environment has been well studied and almost solved. However, in unconstrained environments, the performance of face recognition methods could still be significantly affected by factors such as illumination, pose, resolution, occlusion, aging, etc. In this thesis, we look into the problem of face recognition across these variations and quantization. We present a face recognition algorithm based on simultaneous sparse approximations under varying illumination and pose with dictionaries learned for each class. A novel test image is projected onto the span of the atoms in each learned dictionary. The resulting residual vectors are then used for classification. An image relighting technique based on pose-robust albedo estimation is used to generate multiple frontal images of the same person with variable lighting. As a result, the proposed algorithm has the ability to recognize human faces with high accuracy even when only a single or a very few images per person are provided for training. The efficiency of the proposed method is demonstrated using publicly available databases and it is shown that this method is efficient and can perform significantly better than many competitive face recognition algorithms. The problem of recognizing facial images across aging remains an open problem. We look into this problem by studying the growth in the facial shapes. Building on recent advances in landmark extraction, and statistical techniques for landmark-based shape analysis, we show that using well-defined shape spaces and its associated geometry, one can obtain significant performance improvements in face verification. Toward this end, we propose to model the facial shapes as points on a Grassmann manifold. The face verification problem is then formulated as a classification problem on this manifold. We then propose a relative craniofacial growth model which is based on the science of craniofacial anthropometry and integrate it with the Grassmann manifold and the SVM classifier. Experiments show that the proposed method is able to mitigate the variations caused by the aging progress and thus effectively improve the performance of open-set face verification across aging. In applications such as document understanding, only binary face images may be available as inputs to a face recognition algorithm. We investigate the effects of quantization on several classical face recognition algorithms. We study the performances of PCA and multiple exemplar discriminant analysis (MEDA) algorithms with quantized images and with binary images modified by distance and Box-Cox transforms. We propose a dictionary-based method for reconstructing the grey scale facial images from the quantized facial images. Two dictionaries with low mutual coherence are learned for the grey scale and quantized training images respectively using a modified KSVD method. A linear transform function between the sparse vectors of quantized images and the sparse vectors of grey scale images is estimated using the training data. In the testing stage, a grey scale image is reconstructed from the quantized image using the transform matrix and normalized dictionaries. The identities of the reconstructed grey scale images are then determined using the dictionary-based face recognition (DFR) algorithm. Experimental results show that the reconstructed images are similar to the original grey-scale images and the performance of face recognition on the quantized images is comparable to the performance on grey scale images. The online social network and social media is growing rapidly. It is interesting to study the impact of social network on computer vision algorithms. We address the problem of automated face recognition on a social network using a loopy belief propagation framework. The proposed approach propagates the identities of faces in photos across social graphs. We characterize its performance in terms of structural properties of the given social network. We propose a distance metric defined using face recognition results for detecting hidden connections. The performance of the proposed method is analyzed on graph structure networks, scalability, different degrees of nodes, labeling errors correction and hidden connections discovery. The result demonstrates that the constraints imposed by the social network have the potential to improve the performance of face recognition methods. The result also shows it is possible to discover hidden connections in a social network based on face recognition