  Face recognition systems have become increasingly vulnerable to security
threats in recent years, prompting the use of Face Anti-spoofing (FAS) to
protect against various types of attacks, such as phone unlocking, face
payment, and self-service security inspection. While FAS has demonstrated its
effectiveness in traditional settings, securing it in long-distance
surveillance scenarios presents a significant challenge. These scenarios often
feature low-quality face images, necessitating the modeling of data uncertainty
to improve stability under extreme conditions. To address this issue, this work
proposes Distributional Estimation (DisE), a method that converts traditional
FAS point estimation to distributional estimation by modeling data uncertainty
during training, including feature (mean) and uncertainty (variance). By
adjusting the learning strength of clean and noisy samples for stability and
accuracy, the learned uncertainty enhances DisE's performance. The method is
evaluated on SuHiFiMask [1], a large-scale and challenging FAS dataset in
surveillance scenarios. Results demonstrate that DisE achieves comparable
performance on both ACER and AUC metrics.
