To address the problems that there is often no sufficient class-label information of face samples in face recognition application and some relativity also exist among face sample features, a semi-supervised discriminant analysis based on Mahalanobis distance was presented. The method makes use of the Mahalanobis distance to perform marginal fisher analysis (MFA) for labeled samples in the data set, which is on the basis of the graph embedding framework, so that it not only preserves the intraclass compactness and the interclass separability, but also extracts the discriminant characteristics for effective classification, and simultaneously the unlabeled samples were utilized to characterize the geometric structure of the data set, and thus the local neighborhood information among samples was well preserved. Compared with the traditional feature extraction methods, the proposed method has better recognition performance, and the experiments on ORL, YALE and AR face databases demonstrate the effectiveness of this method.
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