Kernel Bilinear Discriminant Analysis for Face Recognition
Xiao-Zhang Liu · 2017
This paper proposes a novel kernel-based image subspace learning method for face recognition, by encoding an face image as a tensor of second order (matrix). First, we propose a kernel based discriminant tensor criterion, called kernel bilinear fisher criterion (KBFC), which is designed to simultaneously pursue two projection vectors to maximize the interclass scatter and at the same time minimize the intraclass scatter in its corresponding subspace. Then, a score level fusion method is presented to combine two separate projection results to achieve classification tasks. Experimental results on the ORL and UMIST face databases show the effectiveness of the proposed approach.