Authors: Xiao-Zhang Liu; Hang-Yu Ruan
Addresses: College of Information Science and Technology, Hainan University, Haikou, China ' College of Information Science and Technology, Hainan University, Haikou, China
Abstract: This paper proposes a novel kernel-based image subspace learning method for face recognition, by encoding a 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 maximise the interclass scatter and at the same time minimise 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.
Keywords: kernel; tensor discriminant; bilinear discriminant; matrix representation; face recognition.
International Journal of Computational Science and Engineering, 2019 Vol.19 No.2, pp.293 - 300
Received: 23 Jun 2017
Accepted: 01 Nov 2017
Published online: 17 Jun 2019 *