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.
About author:: 马 晓(通讯作者),男,1990年生,博士研究生,主要研究方向为机器学习、模式识别、子空间理论.E-mail:maxiao2012@pku.edu.cn. (MA Xiao (Corresponding author), born in 1990, Ph.D. candidate. His research interests include machine learning, pattern recognition and subspace theory.) 庄雯璟,女,1989年生,硕士研究生,主要研究方向为图像处理、模式识别.E-mail:zhuangwj@pku.edu.cn. (ZHUANG Wenjing, born in 1989, master student. Her research interests include image processing and pattern recognition.) 封举富,男,1967年生,博士,教授,主要研究方向为图像处理、模式识别、机器学习、生物特征识别.E-mail:fjf@cis.pku.edu.cn. (FENG Jufu, born in 1967, Ph.D., professor. His research interests include image processing, pattern recognition, machine learning and biometrics recognition.)
MA Xiao,ZHUANG Wenjing,FENG Jufu. Loose Sparse Representation Based Undersampled Face Recognition with Auxiliary Dictionaries[J]. , 2016, 29(5): 439-446.
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2016, Vol. 29 
