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Proceedings Paper

Training support vector machines for video-based face recognition
Author(s): Li Zhuang; Haizhou Ai; Guangyou Xu
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Paper Abstract

In this paper the problem of training Support Vector Machines (SVMs) for video basedface recognition is presented. Faces as training samples are automatically extractedfrom input video sequence by multiple related template matching and normalized both in geometry via ffIne transformation based on corresponding facial feature points detected in the Sobel convolvedface regions and in gray level distribution via linear transformation to the same average and squared difference. Two different strategies for q-class face recognition problems with SVM are discussed both for ensemble face f eature set andfor PCA compressedfeature set. The performance ofa prototype system based on this technology over 100 clients is reported to demonstrate its greatpotentials in future.

Paper Details

Date Published: 31 July 2002
PDF: 7 pages
Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002); doi: 10.1117/12.477062
Show Author Affiliations
Li Zhuang, Tsinghua Univ. (China)
Haizhou Ai, Tsinghua Univ. (China)
Guangyou Xu, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 4875:
Second International Conference on Image and Graphics
Wei Sui, Editor(s)

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