Face recognition with support vector machine

被引：0

作者：

Zhang, SY ^{[1
]}

Qiao, H ^{[1
]}

机构：

[1] Univ Manchester, Inst Sci & Technol, Dept Computat, Manchester M60 1QD, Lancs, England

来源：

2003 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS, INTELLIGENT SYSTEMS AND SIGNAL PROCESSING, VOLS 1 AND 2, PROCEEDINGS | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The application of Support Vector Machines (SVMs) in face recognition is investigated in this paper. SVM is a classification algorithm recently developed by V. Vapnik and his team. Based on the underlying optimization and statistical learning theories, SVMs provide a new approach to the problem of pattern recognition. In this paper, both linear and nonlinear SVM training models are used in face recognition. Faces in different orientations are taken as training samples. Primary results show that nonlinear training machine is better than linear machine; the former one always has a much larger margin, which means that it has a much stronger ability in classification and recognition.

引用

页码：726 / 730

页数：5

共 16 条

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