MPCA on Gabor Tensor for Face Recognition

摘要

There is a growing interest in subspace learning techniques for face recognition. This paper proposes a novel face recognition method based on MPCA with Gabor tensor representation. Although the Gabor face representation has achieved great success in face recognition, the excessive dimension of the data space often brings the algorithms into the curse of dimensionality dilemma. In this paper, we propose a 3rd-order Gabor tensor representation derived from a complete response set of 40 Gabor filters. Then MPCA (Multi-linear Principal Component Analysis) is applied to each Gabor tensor to extract three discriminative subspaces. The dimension reduction is done in such a way that most useful information is retained. The subspaces are finally integrated for classification. Experimental results on ORL database show promising results of the proposed method.