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Support vector machines for face recognition
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DOI：10.1016/S0262-8856(01)00046-4.png)
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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). (C) 2001 Elsevier Science B.V. All rights reserved.
Keyword:
face recognition
support vector machines
optimal separating hyperplane
learning networks
binary tree
eigenfaces
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