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A robust sparse representation algorithm based on adaptive joint dictionary
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DOI：10.1049/cit2.12092.png)
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Abstract
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Sparse representation based on dictionary construction and learning methods have aroused interests in the field of face recognition. Aiming at the shortcomings of face feature dictionary not 'clean' and noise interference dictionary not 'representative' in sparse representation classification model, a new method named as robust sparse representation is proposed based on adaptive joint dictionary (RSR-AJD). First, a fast low-rank subspace recovery algorithm based on LogDet function (Fast LRSR-LogDet) is proposed for accurate low-rank facial intrinsic dictionary representing the similar structure of human face and low computational complexity. Then, the Iteratively Reweighted Robust Principal Component Analysis (IRRPCA) algorithm is used to get a more precise occlusion dictionary for depicting the possible discontinuous interference information attached to human face such as glasses occlusion or scarf occlusion etc. Finally, the above Fast LRSR-LogDet algorithm and IRRPCA algorithm are adopted to construct the adaptive joint dictionary, which includes the low-rank facial intrinsic dictionary, the occlusion dictionary and the remaining intra-class variant dictionary for robust sparse coding. Experiments conducted on four popular databases (AR, Extended Yale B, LFW, and Pubfig) verify the robustness and effectiveness of the authors' method.
Keywords:
facial recognition
feature extraction
noise dictionary
robust regression
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