Training support vector machines for video-based face recognition

Li Zhuang; Haizhou Ai; Guangyou Xu

doi:10.1117/12.477062

31 July 2002 Training support vector machines for video-based face recognition

Li Zhuang, Haizhou Ai, Guangyou Xu

Proceedings Volume 4875, Second International Conference on Image and Graphics; (2002) https://doi.org/10.1117/12.477062
Event: Second International Conference on Image and Graphics, 2002, Hefei, China

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.

Citation Download Citation

Li Zhuang, Haizhou Ai, and Guangyou Xu "Training support vector machines for video-based face recognition", Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002); https://doi.org/10.1117/12.477062

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