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Proceedings Paper

Automatic video-based face verification and recognition by support vector machines
Author(s): Gang Song; Haizhou Ai; Guangyou Xu; Li Zhuang
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Paper Abstract

This paper presents an automatic video based face verification and recognition system by Support Vector Machines (SVMs). Faces as training samples are automatically extracted from input video sequences in real-time by LUT-based Adaboost and are normalized both in geometry and in gray level distribution after facial landmark localization via Simple Direct Appearance Model (SDAM). Two different strategies for multi-class face verification and recognition problems with SVMs, "one-vs-all" and "one-vs-another", are discussed and compared in details. Experiment results over 100 clients are reported to demonstrate the effectiveness of SVM on video sequences.

Paper Details

Date Published: 25 September 2003
PDF: 6 pages
Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); doi: 10.1117/12.538886
Show Author Affiliations
Gang Song, Tsinghua Univ. (China)
Haizhou Ai, Tsinghua Univ. (China)
Guangyou Xu, Tsinghua Univ. (China)
Li Zhuang, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 5286:
Third International Symposium on Multispectral Image Processing and Pattern Recognition
Hanqing Lu; Tianxu Zhang, Editor(s)

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