Pose robust face recognition based on kernel regression in Bayesian framework

Abstract

To reduce the effect brought by variation of pose, a face recognition system is proposed, in which the correlation between two faces by probabilistic models is computed and the gallery is extended with profile faces generated by a regression-based method. Each face is divided into 3 × 3 regions, and by measuring the distance between the two corresponding regions the distributions of similarities are learned and Bayesian models are formed for each region. These probabilistic models are used to determine whether two faces belong to the same subject. Besides, the gallery is expanded with profile facial images. With this approach, the landmarks on non-frontal faces can be estimated from their frontal landmarks by the mappings learned offline via kernel rank reduced regression (KRRR). Then the profile images are achieved by piecewise affine warping from the corresponding frontal face. Experiments indicate the proposed method performed well, especially when the head rotation angle is large.