We present a novel feature extraction method named local patterns of gradients (LPOGs) for robust face recognition. LPOG uses block-wised elliptical local binary patterns (BELBP), a refined variant of ELBP, and local phase quantization (LPQ) operators directly on gradient images for capturing local texture patterns to build up a feature vector of a face image. From one input image, two directional gradient images are computed. A symmetric pair of BELBP and a LPQ operator are then separately applied upon each gradient image to generate local patterns images. Histogram sequences of local patterns images’ nonoverlapped subregions are finally concatenated to form the LPOG vector for the given image. Based on LPOG descriptor, we propose a novel face recognition system which exploits whitened principal component analysis (WPCA) for dimension reduction and weighted angle-based distance for classification. Experimental results on three large public databases (FERET, AR, and SCface) prove that LPOG WPCA system is robust against a wide range of challenges, such as illumination, expression, occlusion, pose, time-lapse variations, and low resolution. In addition, comparison with other systems shows that LPOG WPCA significantly outperforms the state-of-the-art methods. Computationally, timing benchmarks also demonstrate that our LPOG method is faster than many advanced feature extraction algorithms and can be applied in real-world applications.

Local Patterns of Gradients for Face Recognition

Kernel Locality Preserving Symmetrical Weighted Fisher Discriminant Analysis based subspace approach for expression recognition

Centered around feature extraction, the core task of any Face recognition system, our objective is devising a robust facial representation against major challenges, such as variations of illumination, pose and time-lapse and low resolution probe images, to name a few. Besides, fast processing speed is another crucial criterion. Towards these ends, several methods have been proposed through out this thesis. Firstly, based on the orientation characteristics of the facial information and important features, like the eyes and mouth, a novel variant of LBP, referred as ELBP, is designed for encoding micro patterns with the usage of an horizontal ellipse sample. Secondly, ELBP is exploited to extract local features from oriented edge magnitudes images. By this, the Elliptical Patterns of Oriented Edge Magnitudes (EPOEM) description is built. Thirdly, we propose a novel feature extraction method so called Patch based Local Phase Quantization of Monogenic components (PLPQMC). Lastly, a robust facial representation namely Local Patterns of Gradients (LPOG) is developed to capture meaningful features directly from gradient images. Chiefs among these methods are PLPQMC and LPOG as they are per se illumination invariant and blur tolerant. Impressively, our methods, while offering comparable or almost higher results than that of existing systems, have low computational cost and are thus feasible to deploy in real life applications.

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Contributions to facial feature extraction for face recognition

A two-dimensional principal component analysis (2D PCA) method directed at processing digital images is discussed. The method is based on representation of images as a set of rows and columns analyzing these sets. Two methods of realizing the 2D PCA corresponding to the parallel and cascade forms of its realization are presented, and their characteristics are estimated. The application of the 2D PCA method is shown for solving problems of representation and recognition of facial images. The experiments are fulfilled on ORL and FERET bases.

Application of two-dimensional principal component analysis for recognition of face images

Ao longo dos anos ha um crescente incentivo ao uso da biometria com iniciativas a aperfeicoar, e ate mesmo substituir, os metodos tradicionais de seguranca. Dentre as modalidades biometricas, a face destaca-se por ser comumente vista e utilizada em nossa rotina diariamente. Esta modalidade apresenta bons resultados quando apresentada a ambientes controlados, entretanto, em aplicacoes do mundo real, nao apresenta resultados satisfatorios. Isso, devido a variacoes de iluminacao, expressao, pose e oclusao encontradas ao coletar as imagens de faces nesses cenarios. Comparada com as outras variacoes a oclusao e relativamente pouco estudada na area. Mediante esta problematica, este trabalho tem como objetivo principal investigar, avaliar, comparar e propor tecnicas para deteccao e reconstrucao de oclusoes parciais em imagens de face visando o reconhecimento biometrico.

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Reconstrução de oclusões parciais em imagens de face visando o reconhecimento biométrico

Face recognition has been an important issue in computer vision and pattern recognition over the last several decades (Zhao et al., 2003). While human can recognize faces easily, automated face recognition remains a great challenge in computer-based automated recognition research. One difficulty in face recognition is how to handle the variations in expression, pose and illumination when only a limited number of training samples are available. Currently,face recognition methods can be grouped into three categories, feature-based, holistic-based, and hybrid approaches (Zhao et al., 2003). Feature-based approaches, which extract local features such as the locations and local statistics of the eyes, nose, and mouth, had been investigated in the beginning of the face recognition research (Kanade, 1973). Recently, with the introduction of elastic bunch graph matching (Wiskott, 1997) and local binary pattern (Timo, 2004), local feature-based approaches have shown promising results in face recognition. Holistic-based approaches extract a holistic representation of the whole face region, and have robust recognition performance under noise, blurring, and partial occlusion. After the introduction of Eigenfaces (Turk & Pentland, 1991) and Fisherfaces (Belhumeur et al., 1997), holistic-based approaches were extensively studied and widely applied to face recognition. Motivated by human perception system, hybrid approaches use both local feature and the whole face region for face recognition, and thus are expected to be potentially effective in improving recognition accuracy. In holistic-based face recognition, feature extraction is fundamental, which can be revealed from three aspects. First, the input facial image is high dimensional and most current recognition approaches suffer from the “curse of dimensionality” problem. Thus a feature extraction step is necessary. Second, facial image usually contains less discriminative or unfavorable information for recognition (e.g., illumination). By making use of feature extraction, this information can be efficiently suppressed while retaining discriminative information. Third, feature extraction can greatly reduce the dimensionality of facial image, and this reduces the system’s memory and computational requirements. Subspace method, which aims to reduce the dimension of the data while retaining the statistical separation property between distinct classes, has been a natural choice for facial feature extraction. Face images, however, are generally high dimensional and their withinclass variations is much larger than the between-class variations, which will cause the serious performance degradation of classical subspace methods. By far, various subspace methods have been proposed and applied to face recognition. O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

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Kuanquan Wang

Papers

Subspace Methods for Face Recognition: Singularity, Regularization, and Robustness