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Volume 4 (2009)

	Volume 4, Issue 2 Pg 73 - 159 (November 2009)
	Volume 4, Issue 1 Pg 1 - 71 (August 2009)

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Far East Journal of Experimental and Theoretical Artificial Intelligence

Far East Journal of Experimental and Theoretical Artificial Intelligence
Volume 4, Issue 2, Pages 89 - 101 (November 2009)

FACE RECOGNITION BASED ON MULTI-CLASS SUPPORT VECTOR MACHINE AND FISHER LINEAR DISCRIMINANT METHOD
Deng-Yuan Huang (Taiwan), Wu-Chih Hu (Taiwan), Chun-Jih Lin (Taiwan) and Mu-Song Chen (Taiwan)

Abstract:
In this paper, we present an efficient method of LDA+SVM with two-scale Haar wavelet transformation for face recognition. The performance is evaluated by the ORL and MIT-CBCL databases. The dimensionality reduction of face feature spaces is first carried out by the PCA, LDA and D-LDA methods to form a discriminative feature vector. The face classifications are then performed by the methods of support vector machine (SVM), Euclidean distance, and cosine distance. The recognition accuracies of LDA+SVM achieve 96% and 97% for the evaluations of the ORL and MIT-CBCL databases, respectively. This result also shows the excellence of the proposed method.

Keywords and phrases: face recognition, PCA, LDA, SVM.

Communicated by Chuan-Yu Chang

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