Face Spoofing Detection by Fusing Binocular Depth and Spatial Pyramid Coding Micro-Texture Features
This work addresses face spoofing detection for security applications, presenting an incremental improvement by fusing depth and texture features.
The paper tackles face spoofing detection by proposing two novel features: Template Face Matched Binocular Depth (TFBD) and Spatial Pyramid Coding Micro-Texture (SPMT), which are fused for multi-modal detection. The results show that this fusion achieves strong robustness and time efficiency, outperforming other state-of-the-art traditional methods on widely used and self-constructed datasets.
Robust features are of vital importance to face spoofing detection, because various situations make feature space extremely complicated to partition. Thus in this paper, two novel and robust features for anti-spoofing are proposed. The first one is a binocular camera based depth feature called Template Face Matched Binocular Depth (TFBD) feature. The second one is a high-level micro-texture based feature called Spatial Pyramid Coding Micro-Texture (SPMT) feature. Novel template face registration algorithm and spatial pyramid coding algorithm are also introduced along with the two novel features. Multi-modal face spoofing detection is implemented based on these two robust features. Experiments are conducted on a widely used dataset and a comprehensive dataset constructed by ourselves. The results reveal that face spoofing detection with the fusion of our proposed features is of strong robustness and time efficiency, meanwhile outperforming other state-of-the-art traditional methods.