A Novel Subspace-Based Facial Discriminant Feature Extraction Method

This paper presented a novel subspace-based facial discriminant feature extraction method, i.e. orthogonalized direct linear discriminant analysis (OD-LDA), whose discriminant vectors could be obtained by performing Gram-Schmidt orthogonal procedure on a set of discriminant vectors of D-LDA. Experimental studies conducted on ORL, FERET, Yale, and AR face image databases showed that OD-LDA could compete with prevailing subspace-based facial discriminant feature extraction methods such as Fisherfaces, N-LDA D-LDA, Uncorrelated LDA, parameterized D-LDA, K-L expansion based the between-class scatter matrix, and orthogonal complimentary space method in terms of recognition rate.