Face pose estimation based on kernelized maximum separability

Abstract

Face pose estimation from 2D images is an important topic in the field of computer vision. However, the distribution of face images, under pose variations, is highly nonlinear and complex. We deal with this problem based on the following understanding: (1) the essence of face pose estimation is to assign a face image to a pose template, and the variance of pose templates should be as large as possible; (2) kernel trick is a good auxiliary tool for describing nonlinear distribution. In this paper, we propose a face pose estimation method based on maximum separability of pose templates and incorporate the kernel technique to help model the nonlinear distribution of face patterns under pose variations. The proposed method involves a kernel subspace projection phase and the nearest neighbor classification. Experimental results on the CMU PIE face database and the UMIST database show that the proposed method can achieve high pose estimation performance.