Novel approach to pose invariant face recognition

Face verification in the wild, remains a challenging problem. This paper makes two contributions: first, for improving face recognition in the wild, at least in terms of pose variations, we propose a method for aligning faces by employing single-3D face model as reference produced by FaceGen Modeller. Second, we develop a novel face descriptor based on Gabor Filters. The proposed descriptor relies on combination of Gabor magnitude and Gabor phase informations into an unified framework, which is capable to overcome standard representations in the most popular benchmark “Labeled Faces in the Wild” (LFW). This compact descriptor has a better recognition performance, reaches an accuracy of 97.29% on the LFW dataset.