Face Recognition Method of Mixed Structured SparsityBased on Coding Complexity
CAI Ti-Jian1,2, FAN Xiao-Ping2,3, XIE Xin2, XU Jun2
1.School of Information Science and Engineering, Central South University, Changsha 410012 2.School of Information Engineering, East China JiaoTong University, Nanchang 330013 3.Laboratory of Networked Systems, Hunan University of Finance and Economics, Changsha 410205
Abstract Coding complexity is utilized to represent the structural sparsity, and structural sparsity is achieved by means of reducing coding complexity. Based on the model of sparse representation classification, a structural dictionary is formed from clustering and sorting, sparsity model with mixed structure is constructed. This model combines fixed-length group structure between classes, and dynamic group structure within classes, as well as standard spare structure corresponding to error part. To reconstitute this mixed structural sparsity, an improved mixed structural greedy algorithm is proposed. Experimental results show that the clustering and sorting of the data dictionary can effectively improve the performance of face recognition. Under the same conditions, the performance of mixed structure is better than other structures, and the proposed algorithm outperforms other algorithms.
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2015, Vol. 28 
