Center-Based Line Neighborhood Discriminant Embedding Algorithm and Its Application to Face Recognition
YANG Zhang-Jing1, HUANG Pu2, ZHANG Fan-Long1, YANG Guo-Wei1
1.School of Technology, Nanjing Audit University, Nanjing 211815 2.School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023
Abstract To overcome the drawbacks of the existing marginal fisher analysis algorithm in feature extraction, a center-based line neighborhood discriminant embedding (CLNDE) algorithm is proposed for face recognition. Firstly, the distance from a sample point to the center-based line is utilized to construct the within-class similarity matrix and the between-class similarity matrix, respectively. Next, the between-class local scatter and the within-class local scatter of samples are calculated by the constructed similarity matrices. Finally, the optimal transformation matrix is found by maximizing the between-class local scatter and minimizing the within-class local scatter simultaneously. Experimental results on face databases demonstrate the superiority of the proposed algorithm.
YANG Zhang-Jing,HUANG Pu,ZHANG Fan-Long等. Center-Based Line Neighborhood Discriminant Embedding Algorithm and Its Application to Face Recognition[J]. , 2015, 28(12): 1100-1109.
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2015, Vol. 28 
