# Towards Fair Face Verification: An In-depth Analysis of Demographic Biases arXiv: https://arxiv.org/abs/2307.10011 ยท HTML: https://mlsanity.com/paper/2307.10011/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 42 references that are in the map. - First posted on arXiv 19 Jul 2023. ## Builds on (most cited first, 20 of 20) - [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015) - [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017) - [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016) - [Large-Margin Softmax Loss for Convolutional Neural Networks](https://mlsanity.com/paper/1612.02295/) (2016) - [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018) - [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015) - [Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning](https://mlsanity.com/paper/1911.10692/) (2019) - [A Survey on Bias in Visual Datasets](https://mlsanity.com/paper/2107.07919/) (2021) - [Face Recognition: Too Bias, or Not Too Bias?](https://mlsanity.com/paper/2002.06483/) (2020) - [Towards causal benchmarking of bias in face analysis algorithms](https://mlsanity.com/paper/2007.06570/) (2020) - [Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?](https://mlsanity.com/paper/1912.07398/) (2019) - [Meta Balanced Network for Fair Face Recognition](https://mlsanity.com/paper/2205.06548/) (2022) - [PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition](https://mlsanity.com/paper/2108.03764/) (2021) - [How Does Gender Balance In Training Data Affect Face Recognition Accuracy?](https://mlsanity.com/paper/2002.02934/) (2020) - [Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Are](https://mlsanity.com/paper/2008.06989/) (2020) - [Longitudinal Study of Child Face Recognition](https://mlsanity.com/paper/1711.03990/) (2017) - [Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study](https://mlsanity.com/paper/2005.07302/) (2020) - [FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations](https://mlsanity.com/paper/2304.14252/) (2023) - [Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition](https://mlsanity.com/paper/1703.08617/) (2017) - [Understanding bias in facial recognition technologies](https://mlsanity.com/paper/2010.07023/) (2020) ## Built on by (most cited first, 14 of 14) - [Review of Demographic Fairness in Face Recognition](https://mlsanity.com/paper/2502.02309/) (2025) - [BAdd: Bias Mitigation through Bias Addition](https://mlsanity.com/paper/2408.11439/) (2024) - [MAVias: Mitigate any Visual Bias](https://mlsanity.com/paper/2412.06632/) (2024) - [Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification](https://mlsanity.com/paper/2412.03349/) (2024) - [FaceX: Understanding Face Attribute Classifiers through Summary Model Explanations](https://mlsanity.com/paper/2412.07313/) (2024) - [VB-Mitigator: An Open-source Framework for Evaluating and Advancing Visual Bias Mitigation](https://mlsanity.com/paper/2507.18348/) (2025) - [Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression](https://mlsanity.com/paper/2506.11627/) (2025) - [Demographic Fairness in Multimodal LLMs: A Benchmark of Gender and Ethnicity Bias in Face Verification](https://mlsanity.com/paper/2603.25613/) (2026) - [Investigating Bias and Fairness in Appearance-based Gaze Estimation](https://mlsanity.com/paper/2604.10707/) (2026) - [Toward Fairer Face Recognition Datasets](https://mlsanity.com/paper/2406.16592/) (2024) - [Reliable and Reproducible Demographic Inference for Fairness in Face Analysis](https://mlsanity.com/paper/2510.20482/) (2025) - [Designing and Generating Diverse, Equitable Face Image Datasets for Face Verification Tasks](https://mlsanity.com/paper/2511.17393/) (2025) - [NAMESAKES: Probing Identity Memorization in Text-to-Image Models](https://mlsanity.com/paper/2606.20155/) (2026) - [Face Age Verification Vulnerabilities Under Simple Appearance Manipulations](https://mlsanity.com/paper/2607.24194/) (2026) ## Abstract (arXiv) Deep learning-based person identification and verification systems have remarkably improved in terms of accuracy in recent years; however, such systems, including widely popular cloud-based solutions, have been found to exhibit significant biases related to race, age, and gender, a problem that requires in-depth exploration and solutions. This paper presents an in-depth analysis, with a particular emphasis on the intersectionality of these demographic factors. Intersectional bias refers to the performance discrepancies w.r.t. the different combinations of race, age, and gender groups, an area relatively unexplored in current literature. Furthermore, the reliance of most state-of-the-art approaches on accuracy as the principal evaluation metric often masks significant demographic disparities in performance. To counter this crucial limitation, we incorporate five additional metrics in our quantitative analysis, including disparate impact and mistreatment metrics, which are typically ignored by the relevant fairness-aware approaches. Results on the Racial Faces in-the-Wild (RFW) benchmark indicate pervasive biases in face recognition systems, extending beyond race, with different demographic factors yielding significantly disparate outcomes. In particular, Africans demonstrate an 11.25% lower True Positive Rate (TPR) compared to Caucasians, while only a 3.51% accuracy drop is observed. Even more concerning, the intersections of multiple protected groups, such as African females over 60 years old, demonstrate a +39.89% disparate mistreatment rate compared to the highest Caucasians rate. By shedding light on these biases and their implications, this paper aims to stimulate further research towards developing fairer, more equitable face recognition and verification systems.