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Joint dynamic sparse representation for multi-view face recognition
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DOI：10.1016/j.patcog.2011.09.009.png)
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We consider the problem of automatically recognizing a human face from its multi-view images with unconstrained poses. We formulate the multi-view face recognition task as a joint sparse representation model and take advantage of the correlations among the multiple views for face recognition using a novel joint dynamic sparsity prior. The proposed joint dynamic sparsity prior promotes shared joint sparsity patterns among the multiple sparse representation vectors at class-level, while allowing distinct sparsity patterns at atom-level within each class to facilitate a flexible representation. Extensive experiments on the CMU Multi-PIE face database are conducted to verify the efficacy of the proposed method. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Multi-view face recognition
Joint dynamic sparsity
Joint dynamic sparse representation based classification
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