Face Alignment under Partial Occlusion in near Infrared Images

Face alignment and recognition in less controlled environment are one of the most essential bottlenecks for practical face recognition system. Recently several researches have focused on partial face recognition problem, but few works have addressed the problem of face alignment under partial occlusion. In this paper, we present a robust face alignment method by combining local feature matching and Probabilistic Hough Transform (PHT) for partial face alignment in near infrared (NIR) images. Given a set of well aligned faces as target, and for face images with occlusions, their correspondences are established by local feature matching. For faces with missing components, many false matches of local features will be built due to lack of holistic information. The PHT approach aims to find correct correspondences and resist the inevitable false ones by taking each parameter candidate generated by correspondences pair as a vote in the 4-D in-plane transform parameter space. We also employ geometric constraints and appearance consistency and combine them with PHT in an probabilistic hough optimization function, so that each vote is weighted by a probabilistic score. Experiments of alignment on both MBGC portal face video and facial images with Glass-face occlusions show that our approach can reliably and accurately deal with missing data of facial components caused by partial occlusion.