Robust cross-pose face recognition using landmark oriented depth warping

Abstract

A novel approach exploiting facial landmarks and depth warping is proposed for robust cross-pose face recognition. Unlike the existing 3-D reconstruction based cross-pose recognition algorithms, the proposed algorithm utilizes the automatically identified extensive facial landmarks to replace the computationally expensive 3-D reconstruction procedure, by depth warping. The given face is thereby registered to the most similar 3-D reference model. When matching to a probe face image, the registered depth-warped faces in the gallery are rotated to align to the orientation of the probe image, and sparse regression is then used to identify the correct person. Further, to handle the more challenging cases with eyeglasses, we devise and employ an enhanced Regressive Tree Structured Model (RTSM) combined with inpainting procedure, prior to depth warping. The proposed robust cross-pose recognition (RCPR) algorithm is rigorously validated on PIE and Multi-PIE databases, and compared with state-of-the-art contemporary approaches to demonstrate its superior efficacy.