Support vector machines for face recognition

被引：200

作者：

Guo, GD ^{[1
]}

Li, SZ ^{[1
]}

Chan, KL ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

IMAGE AND VISION COMPUTING | 2001年 / 19卷 / 9-10期

关键词：

face recognition; support vector machines; optimal separating hyperplane; learning networks; binary tree; eigenfaces;

D O I：

10.1016/S0262-8856(01)00046-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：631 / 638

页数：8

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