Face detection based on template matching and support vector machines

Author

Ai, Haizhou ; Liang, Luhong ; Xu, Guangyou

Author_Institution

Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China

Volume

1

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

1006

Abstract

A face detection algorithm integrating template matching and support vector machines (SVM) is presented. Two types of templates: eyes-in-whole and face itself, are used for coarse filtering, and the SVM classifier is used for classification. A bootstrap method is used to collect non-face samples for SVM training under a template matching constrained subspace, which greatly reduces the complexity of training the SVM. Comparative experimental results demonstrate its effectiveness

Keywords

face recognition; image classification; image matching; image sampling; learning automata; SVM classifier; bootstrap method; classification; coarse filtering; complexity; constrained subspace; eyes-in-whole template; face detection; face template; non-face samples; support vector machines; template matching; training; Clustering algorithms; Computer science; Face detection; Filtering algorithms; Filtration; Matched filters; Risk management; Subspace constraints; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2001. Proceedings. 2001 International Conference on

Conference_Location

Thessaloniki

Print_ISBN

0-7803-6725-1

Type

conf

DOI

10.1109/ICIP.2001.959218

Filename

959218