Adaptive Face Recognition via Structed Representation  

作　　者：ZHANG Yu-hua ZENG Xiao-ming 

机构地区：[1]Department of Mathematics, Xiamen University

出　　处：《Computer Aided Drafting,Design and Manufacturing》2014年第3期6-12,共7页计算机辅助绘图设计与制造（英文版）

基　　金：Supported by National Natural Science Foundation of China(No.61170324 and No.61100105)

摘　　要：In this paper, we propose a face recognition approach-Structed Sparse Representation-based classification when the measurement of the test sample is less than the number training samples of each subject. When this condition is not satisfied, we exploit Nearest Subspaee approach to classify the test sample. In order to adapt all the eases, we combine the two approaches to an adaptive classification method-Adaptive approach. The adaptive approach yields greater recognition accuracy than the SRC approach and CRC_RLS approach with low ~ample rate on the Extend Yale B dataset. And it is more efficient than other two approaches.In this paper, we propose a face recognition approach-Structed Sparse Representation-based classification when the measurement of the test sample is less than the number training samples of each subject. When this condition is not satisfied, we exploit Nearest Subspaee approach to classify the test sample. In order to adapt all the eases, we combine the two approaches to an adaptive classification method-Adaptive approach. The adaptive approach yields greater recognition accuracy than the SRC approach and CRC_RLS approach with low ~ample rate on the Extend Yale B dataset. And it is more efficient than other two approaches.

关 键 词：face recognition stmcted representation sparse representation adaptive method orthogonal matching pursuit 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]