关键词: 边界Fisher判别分析, 无监督鉴别投影, 半监督, 核空间, 人脸识别

Abstract: In view of the problems of nonlinear feature extraction and use of a few labeled samples in face recognition, a new algorithm of orthogonal optimal semi-supervised discriminant vectors in a kernel space is proposed. Nonlinear kernel mapping is used to map the face data into an implicit feature space. In this space, the MFA can make use of small amount of labeled samples and the UDP can study a large number of unlabeled samples. The object function is defined using the semi-supervised method. Then optimal projection vector is found using orthogonal approach and face recognition is realized. The effectiveness of the proposed methods is validated through the experimental results on ORL and YALE face databases.

Key words: Marginal Fisher Analysis（MFA）, Unsupervised Discriminant Projection（UDP）, semi-supervised, kernel space, face recognition