Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing

By Zezheng Wang, Zitong Yu, Chenxu Zhao, Xiangyu Zhu, Yunxiao Qin, Qiusheng Zhou, Feng Zhou, Zhen Lei. 2020

215 citations recorded by the source corpus.

Face anti-spoofing is critical to the security of face recognition systems. Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing. Despite the great success, most previous works still formulate the problem as a single-frame multi-task one by simply augmenting the loss with depth, while neglecting the detailed fine-grained information and the interplay between facial depths and moving patterns. In contrast, we design a new approach to detect presentation attacks from multiple frames based on two insights: 1) detailed discriminative clues (e.g., spatial gradient magnitude) between living and spoofing face may be discarded through stacked vanilla convolutions, and 2) the dynamics of 3D moving faces provide important clues in detecting the spoofing faces. The proposed method is able to capture discriminative details via Residual Spatial Gradient Block (RSGB) and encode spatio-temporal information from Spatio-Temporal Propagation Module (STPM) efficiently. Moreover, a novel Contrastive Depth Loss is presented for more accurate depth supervision. To assess the efficacy of our method, we also collect a Double-modal Anti-spoofing Dataset (DMAD) which provides actual depth for each sample. The experiments demonstrate that the proposed approach achieves state-of-the-art results on five benchmark datasets including OULU-NPU, SiW, CASIA-MFSD, Replay-Attack, and the new DMAD. Codes will be available at https://github.com/clks-wzz/FAS-SGTD.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Zhen Lei
2. S. Li
3. Guoying Zhao
4. Xiaogang Wang
5. Xiangyu Zhu
6. Yang Yang
7. Yi Yang
8. Qi Tian

In Signal Processing

1. Zhen Lei
2. Guoying Zhao
3. Zitong Yu
4. S. Li
5. Xiaobai Li
6. Xiangyu Zhu
7. Jingang Shi
8. Xuesong Niu

Citations in the corpus

• Detection and Continual Learning of Novel Face Presentation Attacks (2021, background): “Similar to most sub-fields in computer vision, advances in deep learning, which is inspired by the nervous system [17], have led to significant face PAD improvements on benchmark datasets using convolutional neural network-based (CNN) end-to-end representation learning and classification [1, 7, 12, ”
• Face Anti-Spoofing with Human Material Perception (2020, methodology): “Meanwhile, several video-level CNN methods are presented to exploit the dynamic spatio-temporal [9,33,34,35] or rPPG [13,14,12,36,37,38] features for PAD.”
• Towards Fast, Accurate and Stable 3D Dense Face Alignment (2020, background): “, recognition [45,7,26,24,13,51], animation [10], avatar retargeting [9], tracking [50], attribute classification [4,22,21], image restoration [52,12,11], anti-spoofing [49,55,40,54,25].”
• Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation (2023, methodology): “The proposed method may be used to produce fake videos of individuals, which can be detected using some face anti-spoofing techniques[5, 51, 58].”