Face Anti Spoofing
Face anti-spoofing (FAS) aims to secure facial recognition systems by detecting fake faces (spoofs) presented as legitimate identities. Current research heavily focuses on improving the generalization of FAS models across diverse domains (different cameras, lighting, attack types), employing architectures like Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs), often enhanced by techniques such as data augmentation, multimodal fusion, and self-supervised learning. The development of robust and generalized FAS methods is crucial for the widespread adoption of secure facial recognition technologies in various applications, from access control to financial transactions.
Papers
Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
Dong Wang, Jia Guo, Qiqi Shao, Haochi He, Zhian Chen, Chuanbao Xiao, Ajian Liu, Sergio Escalera, Hugo Jair Escalante, Zhen Lei, Jun Wan, Jiankang Deng
Instance-Aware Domain Generalization for Face Anti-Spoofing
Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu, Ran Yi, Shouhong Ding, Lizhuang Ma