PCGattnNet: A 3-D Point Cloud Dynamic Graph Attention for Generalizable Face Presentation Attack Detection

Abstract

Face recognition systems that are commonly used in access control settings are vulnerable to presentation attacks, which pose a significant security risk. Therefore, it is crucial to develop a robust and reliable face presentation attack detection system that can automatically detect these types of attacks. In this paper, we present a novel technique called Point Cloud Graph Attention Network (PCGattnNet) to detect face presentation attacks using 3D point clouds captured from a smartphone. The innovative nature of the proposed technique lies in its ability to dynamically represent point clouds as graphs that effectively capture discriminant information, thereby facilitating the detection of robust presentation attacks. To evaluate the efficacy of the proposed method effectively, we introduced newly collected 3D face point clouds using two different smartphones. The newly collected dataset comprised bona fide samples from 100 unique data subjects and six different 3D face presentation attack instruments. Extensive experiments were conducted to evaluate the generalizability of the proposed and existing methods to unknown attack instruments. The outcomes of these experiments demonstrate the reliability of the proposed method for detecting unknown attack instruments.