Multiple feature subspaces analysis for single sample per person face recognition

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Collecting samples is one of the main difficulties for face recognition, for example, in most of the real-world applications such as law enhancement, e-passport, and ID card identification, it is customary to collect a single sample per person (SSPP). Unfortunately, in such SSPP scenario, many presented face recognition methods suffer serious performance drop or fail due to their inability to learn the discriminative information of a person from a single sample. To address the SSPP problem, in this paper, we propose a multiple feature subspaces analysis (MFSA) approach, which takes advantage of facial symmetry. First, we divide each enrolled face into two halves about the bilateral symmetry axis and further partition every half into several local face patches. Second, we cluster all the patches into multiple groups according to their locations at the half face and formulate SSPP as a MFSA problem by learning a feature subspace for each group, so that the confusion between inter-class and intra-class variations of face patches is removed and more discriminative features can be extracted from each subspace. To recognize a target person, a k-NN classifier is employed in each subspace to predict the label of a face patch and majority voting strategy is used to identify the unlabeled subject. Compared with the state-of-the-art methods, MFSA is effortless and efficient in implementing, but achieves either better or competitive performance when recognizing face images taken in both constrained and unconstrained environment.