Support vector machines for face recognition |
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| Affiliation: | 1. Division Genetics and Genomics, Department of Pediatrics, Boston Children''s Hospital, Pediatrics, Harvard Medical School and Center for Bioethics, Harvard Medical School, Boston, MA, United States;2. Division of Genetics and Genomics, Department of Pediatrics, Boston Children''s Hospital, Harvard Medical School, Boston, MA, United States;3. Department of Pediatrics (Emerita), Department of Pediatrics, and Emeritus Faculty, Center for Pediatric Bioethics, Children''s Hospital Los Angeles, Keck School of Medicine at University Southern California, Los Angeles, CA, United States |
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| Abstract: | Support vector machines (SVMs) have been recently proposed as a new learning network for bipartite pattern recognition. In this paper, SVMs incorporated with a binary tree recognition strategy are proposed to tackle the multi-class face recognition problem. The binary tree extends naturally, the pairwise discrimination capability of the SVMs to the multi-class scenario. Two face databases are used to evaluate the proposed method. The performance of the SVMs based face recognition is compared with the standard eigenface approach, and also the more recently proposed algorithm called the nearest feature line (NFL). |
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