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.
64papers
Papers - Page 3
April 15, 2023
April 12, 2023
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 DengInstance-Aware Domain Generalization for Face Anti-Spoofing
Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu, Ran Yi, Shouhong Ding, Lizhuang Ma
February 17, 2023
February 11, 2023
January 3, 2023
December 7, 2022
Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing
Kaicheng Li, Hongyu Yang, Binghui Chen, Pengyu Li, Biao Wang, Di HuangCyclically Disentangled Feature Translation for Face Anti-spoofing
Haixiao Yue, Keyao Wang, Guosheng Zhang, Haocheng Feng, Junyu Han, Errui Ding, Jingdong Wang
August 28, 2022
August 23, 2022
August 8, 2022
July 4, 2022