A Dual-stream Framework for 3D Mask Face Presentation Attack Detection
Chen Shen, Taiping Yao, Ke-Yue Zhang, Yang Chen, Ke Sun, Shouhong Ding, Jilin Li, Feiyue Huang, Rongrong Ji
Abstract
Face presentation attack detection (PAD) plays a vital role in face recognition systems. Many previous face antispoofing methods mainly focus on the 2D face representation attacks, which however, suffer from great performance degradation when facing high-fidelity 3D mask attacks. To address this issue, we propose a novel dual-stream framework consisting of the vanilla convolution stream and the central difference convolution stream. These two streams complement each other and learn more comprehensive features for 3D mask attacks detection. Moreover, we extend 3D PAD to a multi-classification task that contains real face, plaster attack and transparent attack, and utilize various data augmentations and label smoothing techniques to improve the generalizability on unseen attacks. The proposed method achieved the second place in the Chalearn 3D High-Fidelity Mask Face Presentation Attack Detection Challenge@ICCV2021 with a score of 3.15 (ACER).