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| 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 |
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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.
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Received: 02 March 2015
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2015, Vol. 28 
