Joint dynamic sparse representation for multi-view face recognition

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.

► We consider the task of multi-view face recognition with unconstrained poses.
► We propose a joint dynamic sparsity representation based classification method.
► It jointly selects the most representative gallery faces for each view.
► The proposed method achieves promising results in multi-view face recognition.