AI 服务暂时不可用，以下为来源正文，待恢复后补全翻译。
[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 View PDF HTML (experimental) 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) Full-text links: Access Paper: 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 View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)