3D Facial Geometric Attributes Based Anti-Spoofing Approach against Mask Attacks
Yinhang Tang, Liming Chen · 2017
3D scanning and 3D printing techniques, as the technical impetus of 3D face recognition, also boost unconsciously the security threat against it from the spoofing attacks via manufactured mask. In order to improve the robustness of 3D face recognition system, several countermeasures against mask attacks based on photometric features have been reported in recent years. However, the anti-spoofing approach involving 3D meshed face scan and the related 3D facial features have not been studied yet. For filling this gap, in this paper, we propose to exploit the anti-spoofing performance of geometric attributes based 3D facial description. It synthesises the advantages of the selected geometric attributes, named principal curvature measures, and the meshSIFT-based feature descriptor. Specifically, the estimation of geometric attributes is coherent to the property of discrete surface, and the feature related to them can accurately describe the shape of facial surface. These characteristics are beneficial to discovering the geometry-based dissimilarity between genuine face and fraud mask. In the experiment part, the baselines of verification and anti-spoofing performance are evaluated on the Morpho database. Furthermore, for simulating a real-world scenario and testing the corresponding anti-spoofing performance, the size of genuine face set is massively extended by uniting the Morpho database and the FRGC v2.0 database to increase the ratio of genuine faces to fraud masks. The evaluation results prove that the proposed 3D face verification system can guarantee competitive verification accuracy for genuine faces and promising anti-spoofing performance against mask attacks.