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In this paper, a new similarity measure is developed for huma n face recognition, namely, weighted matrix distance. The key difference between this m etric and the standard distances is the use of matrices and weights rather than the vectors onl y. The two feature matrices are obtained by two-dimensional principal component analysis (2DPCA). The weights are the inverse of the eigenvalues sorted in decreasing order of the co variance matrix of all training face matrices. Experiments are performed under illumination an d f cial expression variations using four face image databases: ORL, Yale, PF01 and a subset of FER ET. The results demonstrate the effectiveness of the proposed weighted matrix distance s in 2DPCA face recognition over the standard matrix distance metrics: Yang, Frobenius and a ssembled matrix distance (AMD).

Efficient face recognition based on weighted matrix distance metrics and 2DPCA algorithm