{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mitigating-gender-bias-in-face-recognition","title":"Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model","arxiv_id":"2210.13664","date":"2022-10-24","proceeding":null,"authors":["Jean-Rémy Conti","Nathan Noiry","Vincent Despiegel","Stéphane Gentric","Stéphan Clémençon"],"abstract":"In spite of the high performance and reliability of deep learning algorithms in a wide range of everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against specific subgroups of the population (e.g. gender, ethnicity). This urges the practitioner to develop fair systems with a uniform/comparable performance across sensitive groups. In this work, we investigate the gender bias of deep Face Recognition networks. In order to measure this bias, we introduce two new metrics, $\\mathrm{BFAR}$ and $\\mathrm{BFRR}$, that better reflect the inherent deployment needs of Face Recognition systems. Motivated by geometric considerations, we mitigate gender bias through a new post-processing methodology which transforms the deep embeddings of a pre-trained model to give more representation power to discriminated subgroups. It consists in training a shallow neural network by minimizing a Fair von Mises-Fisher loss whose hyperparameters account for the intra-class variance of each gender. Interestingly, we empirically observe that these hyperparameters are correlated with our fairness metrics. In fact, extensive numerical experiments on a variety of datasets show that a careful selection significantly reduces gender bias. The code used for the experiments can be found at https://github.com/JRConti/EthicalModule_vMF.","url_abs":"https://arxiv.org/abs/2210.13664v3","url_pdf":"https://arxiv.org/pdf/2210.13664v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mitigating-gender-bias-in-face-recognition","repo_url":"https://github.com/JRConti/EthicalModule_vMF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"face-verification","task_name":"Face Verification"},{"task_slug":"fairness","task_name":"Fairness"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-verification-on-lfw","task":"Face Verification","dataset":"LFW","model":"ArcFaceR50 + EM-FRR","rank_in_archive_order":1,"of":3,"metrics":{"BFAR":"33.65","BFRR":"5.89","FRR@FAR(%)":"0.100"},"uses_additional_data":false},{"leaderboard":"/sota/face-verification-on-lfw","task":"Face Verification","dataset":"LFW","model":"ArcFaceR50 + EM-C","rank_in_archive_order":2,"of":3,"metrics":{"BFAR":"2.44","BFRR":"9.18","FRR@FAR(%)":"0.164"},"uses_additional_data":false},{"leaderboard":"/sota/face-verification-on-lfw","task":"Face Verification","dataset":"LFW","model":"ArcFaceR50 + EM-FAR","rank_in_archive_order":3,"of":3,"metrics":{"BFAR":"2.11","BFRR":"11.22","FRR@FAR(%)":"0.151"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2210.13664","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13664"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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