[Submitted on 14 Sep 2026]
Title:DenseFace: Bias Mitigation in Face Recognition via Density-Aware Probabilistic Matching
View a PDF of the paper titled DenseFace: Bias Mitigation in Face Recognition via Density-Aware Probabilistic Matching, by Mansur Bultygov and 3 other authors
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Abstract:Despite steady progress in face recognition, current face recognition models still suffer from significant demographic biases. While approaches for bias mitigation have been proposed, existing methods often impose constraints on the training procedure and result in the degradation of recognition accuracy. To address this issue, we here introduce a method that reduces racial bias in pre-trained face recognition models without compromising their accuracy. To this end, we model face embeddings of each person by von Mises-Fisher (MF) distribution. We next observe the dependency between demographic attributes and the density of MF distributions, and propose DenseFace, a probabilistic face matching procedure that accounts for differences in MF distributions. Our extensive experiments demonstrate DenseFace to consistently reduce racial bias in strong face recognition models varying in network architectures, training datasets and loss functions. Notably, DenseFace preserves recognition accuracy and requires no retraining of the underlying face recognition model. Our work also investigates previously adopted bias measures and makes suggestions.
Comments: 13 pages, 10 figures. Accepted at IEEE/IAPR International Joint Conference on Biometrics (IJCB) 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.16149 [cs.CV]
(or arXiv:2609.16149v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.16149
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Vadim Selyutin [view email] [v1] Mon, 14 Sep 2026 18:00:44 UTC (2,214 KB)
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