Binary neural network based 3D facial feature localization

In this paper, a methodology for facial feature identification and localization approach is proposed based on binary neural network algorithms. We present a head pose and facial expression invariant 3D shape descriptor called mesh-like multi circle curvature descriptor (MMCCD), which provides more 3D curvature attributes than other similar approaches. To search and match the feature patterns with more attributes, we use advanced uncertain reasoning architecture (AURA) k-nearest neighbour algorithms to encode, train and match the feature patterns based on 3D shape curvature. Experiments performed on the FRGC dataset (4950 3D faces) with pose and expression variations show that our approach is able to achieve an accurate (over 99.69% nose tip identification) and robust identification and localization of facial features.