Face Anti-Spoofing: Model Matters, so Does Data

By Xiao Yang, Wenhan Luo, Linchao Bao, Yuan Gao, Dihong Gong, Shibao Zheng, Zhifeng Li, Wei David Liu. 2019

228 citations recorded by the source corpus.

Face anti-spoofing is an important task in full-stack face applications including face detection, verification, and recognition. Previous approaches build models on datasets which do not simulate the real-world data well (e.g., small scale, insignificant variance, etc.). Existing models may rely on auxiliary information, which prevents these anti-spoofing solutions from generalizing well in practice. In this paper, we present a data collection solution along with a data synthesis technique to simulate digital medium-based face spoofing attacks, which can easily help us obtain a large amount of training data well reflecting the real-world scenarios. Through exploiting a novel Spatio-Temporal Anti-Spoof Network (STASN), we are able to push the performance on public face anti-spoofing datasets over state-of-the-art methods by a large margin. Since the proposed model can automatically attend to discriminative regions, it makes analyzing the behaviors of the network possible.We conduct extensive experiments and show that the proposed model can distinguish spoof faces by extracting features from a variety of regions to seek out subtle evidences such as borders, moire patterns, reflection artifacts, etc.

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. Xiaogang Wang
2. Luc Van Gool
3. Dacheng Tao
4. Xilin Chen
5. Junjie Yan
6. Zhen Lei
7. Yi Yang
8. Wanli Ouyang

In Signal Processing

1. Xilin Chen
2. Shiguang Shan
3. Guoying Zhao
4. Zitong Yu
5. Xiaobai Li
6. Chenxu Zhao
7. Xiangyu Zhu
8. Zhen Lei

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 [23], have led to significant face PAD improvements on benchmark datasets using convolutional neural network-based (CNN) end-to-end representation learning and classification [2, 13, 18,”
• CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations (2020, background): “APCER, BPCER, ACER, EER, and HTER) [4,23,15,36] and add another two criteria (i.”
• Single-Side Domain Generalization for Face Anti-Spoofing (2020, background): “And Yang et al. [43] take into consideration the global temporal and local spatial cues to distinguish The parameter sharing feature generator is trained to make the feature distributions of different domains undistinguishable for the real faces but not for the fake ones under the single-side…”
• CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing (2019, unknown): “STASN [65] 1.”
• Face Anti-Spoofing with Human Material Perception (2020, methodology): “All compared methods including STASN [34] are trained without extra datasets for a fair comparison.”
• Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing (2020, methodology): “More general approaches like 3D convolution [20] or LSTM [50, 53] have recently been used to distinguish the live from spoof images.”