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.

翻译：近年来，人脸识别系统面临日益严峻的安全威胁，促使研究者采用人脸防伪（FAS）技术防范各类攻击，如手机解锁、刷脸支付及自助安检等场景。尽管FAS在传统场景中已展现出有效性，但在远距离监控场景下的安全防护仍面临重大挑战。此类场景常伴随低质量人脸图像，需要通过建模数据不确定性来提升极端条件下的稳定性。为解决该问题，本文提出分布估计（DisE）方法，通过训练过程中对数据不确定性（包括特征均值与方差）进行建模，将传统FAS点估计转化为分布估计。通过调整干净样本与噪声样本的学习强度以兼顾稳定性与准确性，所学习的不确定性可增强DisE的性能。该方法在SuHiFiMask [1]这一大规模、高挑战性的监控场景FAS数据集上进行评估，结果表明DisE在ACER和AUC指标上均取得了具有竞争力的性能。