Abstract
As a dominant method for face recognition, the subspace learning algorithm shows desirable performance. Manifold learning can deal with the nonlinearity hidden in the data, and can project high dimensional data onto low dimensional data while preserving manifold structure. Sparse representation shows its robustness for noises and is very practical for face recognition. In order to extract the facial features from face images effectively and robustly, in this paper, a method called graph regularized within-class sparsity preserving analysis (GRWSPA) is proposed, which can preserve the within-class sparse reconstructive relationship and enhances separatability for different classes. Specifically, for each sample, we use the samples in the same class (except itself) to represent it, and keep the reconstructive weight unchanged during projection. To preserve the manifold geometry structure of the original space, one adjacency graph is constructed to characterize the interclass separability and is incorporated into its criteria equation as a constraint in a supervised manner. As a result, the features extracted are sparse and discriminative and helpful for classification. Experiments are conducted on the two open face databases, the ORL and YALE face databases, and the results show that the proposed method can effectively and correctly find the key facial features from face images and can achieve better recognition rate compared with other existing ones.
Highlights
Face recognition is an important but complicated problem in computer vision
The problem of facial expressions is a major issue in 3D face recognition, since the geometry of the face significantly changes with different facial expressions
Suppose i is the optimal solution to the above optimization, sparsity preserving projection (SPP) tries to preserve the sparse reconstruction relationship, which can be expressed as the following optimization: n min AT xi AT X i i
Summary
Face recognition is an important but complicated problem in computer vision. It has broad applications ranging from computer interface to surveillance. Most of the images can be seen as two-dimensional matrices, so 2D face recognition received tremendous attention in computer vision and pattern recognition Subspace learning methods, such as principle component analysis (PCA) [5] and linear discriminant analysis (LDA) [6], have been extensively studied. It is reported that the face images probably reside in some sort of manifolds [7] One problem of these two algorithms is that they only exploit the linear global Euclidean structure and ignores the local geometry structure. Image, SR is an unsupervised method but it exploits the discriminant nature of sparse representation for classification Based on this idea, Qiao proposed sparsity preserving projection (SPP) [23] for feature extraction, which tries to preserve the sparse reconstructive relationship of samples in the low-dimensional data by minimizing the distance between sparsely reconstructed samples and the original sample.
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