基于非线性特征提取和SVM的人脸识别算法

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Abstract Both PCA and LDA are performed by only using the second-order statistics among image pixels, and not sensitive to high order statistics in the data. In this paper, the kernel function method is used to extract the high order relations, and the Linear Support Vector Machines (LSVM) is selected to perform the face classification. The experiment on Yale face database shows that the nonlinear feature extraction method is effective, and SVM is better than nearest neighbor classifier.

Key words： Nonlinear feature extraction KPCA KDA Linear SVM

Received: 07 October 2002

PACS:	TP391.42
	N911.73

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	Sun Da-rui
	Wu Le-nan

Cite this article:

Sun Da-rui,Wu Le-nan. Face Recognition Based on Nonlinear Feature Extraction and SVM[J]. , 2004, 26(2): 307-311 .

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http://jeit.ie.ac.cn/EN/ OR http://jeit.ie.ac.cn/EN/Y2004/V26/I2/307