A unified 3D face authentication framework based on robust local mesh SIFT feature

In this paper, we design a unified 3D face authentication system for practical use. First, we propose a facial depth recovery method to construct a facial depth map from stereoscopic videos. It effectively utilize prior facial information and incorporate the visibility term to classify static and dynamic pixels for robust depth estimation. Secondly, in order to make 3D face authentication more accurate and consistent, we present an intrinsic scale feature detection for interesting points on 3D facial mesh regions. Then, a novel feature descriptor is proposed, called Local Mesh Scale-Invariant Feature Transform (LMSIFT) to reflect the different face recognition abilities in different facial regions. Finally, the sparse optimization problem of visual codebook is used to 3D face learning. We evaluate our approach on publicly available 3D face databases and self-collected realistic scene databases. We also develop an interactive education system to investigate its performance in practice, which demonstrates the high performance of the proposed approach for accurate 3D face authentication. Compared with previous popular approaches, our system has consistently better performance in terms of effectiveness, robustness and universality.