Liveness Detection Using Implicit 3D Features
This addresses security vulnerabilities in face recognition for applications like authentication, though it is incremental as it builds on existing 2D methods with new features.
The paper tackles spoofing attacks in face recognition systems by proposing a liveness detection method that uses implicit 3D features from images under different illumination or stereo cameras, achieving state-of-the-art results with minimal latency.
Spoofing attacks are a threat to modern face recognition systems. In this work we present a simple yet effective liveness detection approach to enhance 2D face recognition methods and make them robust against spoofing attacks. We show that the risk to spoofing attacks can be re- duced through the use of an additional source of light, for example a flash. From a pair of input images taken under different illumination, we define discriminative features that implicitly contain facial three-dimensional in- formation. Furthermore, we show that when multiple sources of light are considered, we are able to validate which one has been activated. This makes possible the design of a highly secure active-light authentication framework. Finally, further investigating the use of 3D features without 3D reconstruction, we introduce an approximated disparity-based implicit 3D feature obtained from an uncalibrated stereo-pair of cameras. Valida- tion experiments show that the proposed methods produce state-of-the-art results in challenging scenarios with nearly no feature extraction latency.