Face Recognition Based on Generalized Discriminant Analysis
Ying Zhang, Wang Yao-nan · Jisuanji gongcheng · 2008
【Abstract】The method based on Generalized Discriminant Analysis(GDA) is proposed for face recognition. Data points are mapped by means of nonlinear kernel function to high dimensional feature space to solve the problem in linear discriminant analysis algorithm, thus nonlinear characteristics of judgment can be available in input space, which is well adapt to facial illumination, expression, posture and other complicated changes. Experimental results show that GDA can get higher classification accuracy rate than Eigenfaces algorithm and Fisherfaces algorithm with less eigenvectors. 【Key words】Generalized Discriminant Analysis(GDA); face recognition; kernel function; Eigenfaces; Fisherfaces