Loose Sparse Representation Based Undersampled Face Recognition with Auxiliary Dictionaries
MA Xiao, ZHUANG Wenjing, FENG Jufu
Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871 Key Laboratory of Machine Perception Ministry of Education, Peking University, Beijing 100871
Abstract：In the undersampled face recognition problem with uncontrolled intra-class variations, the auxiliary dictionary can not work quite well. The training dictionary and the auxiliary dictionary in the sparse representation face recognition methods have different representation abilities for the query image. Thus, different demands on the sparsity constraints of these dictionaries at representation stage are discussed. In this paper, a loose sparse representation based classification with auxiliary dictionaries (LSRCAD) is proposed by using different constraints on two types of dictionary respectively. The experiments confirm the effectiveness and the robustness of LSRCAD. LSRCAD outperforms the original sparse representation face recognition methods with auxiliary dictionaries for undersampled face recognition problems.
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2016, Vol. 29 
