A Cross-Dataset Evaluation of Anti-Face-Spoofing Methods Using Random Forests and Convolutional Neural Networks
Chin‐Shyurng Fahn, Chu-Ping Lee, Meng-Luen Wu · 2019
Face recognition for authentication, namely unlocking by faces, is widely used in various access control applications, especially in mobile devices, and becomes one of major biometric authentication technology. Some existing authentication methods require additional depth sensors; however, they are still cheated by 2D or 3D printed faces sometimes. Although many researches aim at detecting fake faces, most of them only work well on specific situations, and they are unusable to master unseen spoofed scenarios.