Improving cross-domain face anti-spoofing under different source training data configurations - Cai, Rizhao (Nanyang Technological University, 2024)
Facial Recognition (FR), popular for its user-friendly identity verification, encounters significant risks from face spoofing attacks or Presentation Attacks (PA). Developing face anti-spoofing systems is crucial for FR in security-sensitive environments. Current face anti-spoofing methods are effective in the intra-domain evaluation, where the distribution of test data matches that of training data. However, the distributions differ in the cross-domain evaluation, and there is a significant performance decline in effectiveness. This decline results from exposure to new domain data from unknown distributions, which is unseen during training. The cross-domain problem is the main challenge in face anti-spoofing research.