# Race Bias Analysis of Bona Fide Errors in face anti-spoofing

arXiv: https://arxiv.org/abs/2210.05366 · HTML: https://mlsanity.com/paper/2210.05366/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 52 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 27 of 27)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Searching Central Difference Convolutional Networks for Face Anti-Spoofing](https://mlsanity.com/paper/2003.04092/) (2020)
- [Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision](https://mlsanity.com/paper/1803.11097/) (2018)
- [CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](https://mlsanity.com/paper/2007.12342/) (2020)
- [NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing](https://mlsanity.com/paper/2011.02062/) (2020)
- [face anti-spoofing based on color texture analysis](https://mlsanity.com/paper/1511.06316/) (2015)
- [Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network](https://mlsanity.com/paper/1909.08848/) (2019)
- [Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing](https://mlsanity.com/paper/2003.08061/) (2020)
- [CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing](https://mlsanity.com/paper/2003.05136/) (2020)
- [Face Anti-Spoofing Via Disentangled Representation Learning](https://mlsanity.com/paper/2008.08250/) (2020)
- [CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing](https://mlsanity.com/paper/1908.10654/) (2019)
- [Face De-Spoofing: Anti-Spoofing via Noise Modeling](https://mlsanity.com/paper/1807.09968/) (2018)
- [Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?](https://mlsanity.com/paper/1912.07398/) (2019)
- [Revisiting Pixel-Wise Supervision for Face Anti-Spoofing](https://mlsanity.com/paper/2011.12032/) (2020)
- [Fairness in Biometrics: a figure of merit to assess biometric verification systems](https://mlsanity.com/paper/2011.02395/) (2020)
- [InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics](https://mlsanity.com/paper/2004.06592/) (2020)
- [Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review](https://mlsanity.com/paper/2004.10998/) (2020)
- [DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing](https://mlsanity.com/paper/2009.07529/) (2020)
- [Anomaly Detection-Based Unknown Face Presentation Attack Detection](https://mlsanity.com/paper/2007.05856/) (2020)
- [Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning](https://mlsanity.com/paper/2112.00568/) (2021)
- [Deep Anomaly Detection for Generalized Face Anti-Spoofing](https://mlsanity.com/paper/1904.08241/) (2019)
- [On the Robustness of Face Recognition Algorithms Against Attacks and Bias](https://mlsanity.com/paper/2002.02942/) (2020)
- [Demographic Bias in Presentation Attack Detection of Iris Recognition Systems](https://mlsanity.com/paper/2003.03151/) (2020)
- [Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing](https://mlsanity.com/paper/1907.04048/) (2019)
- [A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing](https://mlsanity.com/paper/1805.04176/) (2018)

## Built on by (most cited first, 1 of 1)

- [Zero-failure testing of binary classifiers](https://mlsanity.com/paper/2407.03979/) (2024)

## Abstract (arXiv)

The study of bias in Machine Learning is receiving a lot of attention in recent years, however, few only papers deal explicitly with the problem of race bias in face anti-spoofing. In this paper, we present a systematic study of race bias in face anti-spoofing with three key characteristics: the focus is on analysing potential bias in the bona fide errors, where significant ethical and legal issues lie; the analysis is not restricted to the final binary outcomes of the classifier, but also covers the classifier's scalar responses and its latent space; the threshold determining the operating point of the classifier is considered a variable. We demonstrate the proposed bias analysis process on a VQ-VAE based face anti-spoofing algorithm, trained on the Replay Attack and the Spoof in the Wild (SiW) databases, and analysed for bias on the SiW and Racial Faces in the Wild (RFW), databases. The results demonstrate that race bias is not necessarily the result of different mean response values among the various populations. Instead, it can be better understood as the combined effect of several possible characteristics of the response distributions: different means; different variances; bimodal behaviour; existence of outliers.