# Distributional Estimation of Data Uncertainty for Surveillance Face Anti-spoofing

arXiv: https://arxiv.org/abs/2309.09485 · HTML: https://mlsanity.com/paper/2309.09485/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [Probabilistic Face Embeddings](https://mlsanity.com/paper/1904.09658/) (2019)
- [Data Uncertainty Learning in Face Recognition](https://mlsanity.com/paper/2003.11339/) (2020)
- [PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition](https://mlsanity.com/paper/2203.14325/) (2022)
- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition](https://mlsanity.com/paper/2205.12010/) (2022)
- [Surveillance Face Anti-spoofing](https://mlsanity.com/paper/2301.00975/) (2023)
- [Uncertainty-Estimation with Normalized Logits for Out-of-Distribution Detection](https://mlsanity.com/paper/2302.07608/) (2023)
- [Improving Training and Inference of Face Recognition Models via Random Temperature Scaling](https://mlsanity.com/paper/2212.01015/) (2022)

## Abstract (arXiv)

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.