On Averaging Face Images for Recognition under Pose Variations

Abstract

Recently, psychological studies showed that averaging human face images greatly improves the performance of face recognition under various pose, illumination, expression, and/or aging conditions. This paper investigates quantitatively the mechanism of the face averaging process in face recognition specifically against pose variations. Facilitated with 3D face dataset, the process of face averaging is tested on face images free from human errors and misalignments. Single images are chosen as gallery and the averaged views as probe in all the experiments. Three different scenarios are experimented, i.e., identification using single gallery images, identification using different gallery images, and averaging using unbalanced range of input image. The experimental results show that the averaging process under pose variations is equivalent to generating a face view in an average pose and the improvement in face recognition is subject to the conditions that the gallery pose is close to the average probe pose.