Robust Principal Component Analysis for Face Subspace Recovery

Subspace method is one of the classical methods in face recognition,which assumes that face images lie in a low-rank subspace.However,due to illumination variation,shadows,occlusion,specularities and corruption,real face images seldom reveal such low-rank structure.We propose a face subspace recovery method based on the Robust Principal Component Analysis.The face image matrix is modeled as the sum of a low-rank matrix and a deviation matrix,in which the low-rank matrix reveals the ideal subspace structure and the deviation matrix accounts for the illumination variation,shadows,occlusion,specularities and corruption.By using the robust principal component analysis,the low-rank matrix and deviation matrix can be recovered efficiently.The experimental results show that this method is efficient in recovering the low-rank face subspaces.