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Face recognition using difference vector plus KPCA |
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| Authors: | Ying WenAuthor Vitae Lianghua HeAuthor Vitae Pengfei ShiAuthor Vitae |
| Affiliation: | a Department of Computer Science and Technology, East China Normal University, Shanghai, 200062, China b Pediatric Brain Imaging Laboratory, Columbia University in the City of New York, NY 10032, USA c Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, 200092, China d Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200030, China |
| Abstract: | In this paper, a novel approach for face recognition based on the difference vector plus kernel PCA is proposed. Difference vector is the difference between the original image and the common vector which is obtained by the images processed by the Gram-Schmidt orthogonalization and represents the common invariant properties of the class. The optimal feature vectors are obtained by KPCA procedure for the difference vectors. Recognition result is derived from finding the minimum distance between the test difference feature vectors and the training difference feature vectors. To test and evaluate the proposed approach performance, a series of experiments are performed on four face databases: ORL, Yale, FERET and AR face databases and the experimental results show that the proposed method is encouraging. |
| Keywords: | Face recognition Common vector Kernel PCA |
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