{"ID":3052320,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-06T05:28:17.175469824Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04469","arxiv_id":"2606.04469","title":"Adaptive Calibration for Fair and Performant Facial Recognition","abstract":"We introduce Adaptive Calibration (AC), a novel calibration strategy for facial recognition that maps cosine similarity between normalized embeddings to well-calibrated probabilities. By incorporating local context into calibration, Adaptive Calibration corrects for a fundamental mismatch in cosine similarity, whereby the same distance can correspond to different match probabilities in different embedding regions. Our approach improves both overall performance and results in a fairer calibration without requiring demographic metadata. Our approach consistently dominates existing methods both on accuracy and fairness metrics across a variety of pretrained models and standard benchmarks. AC provides a practical solution for equitable facial recognition, without requiring demographic group annotations, and while improving overall performance. Unlike existing approaches, our method provides continuous, region-specific calibration that avoids \"leveling down\" where fairness comes at the cost of degraded performance for some groups.","short_abstract":"We introduce Adaptive Calibration (AC), a novel calibration strategy for facial recognition that maps cosine similarity between normalized embeddings to well-calibrated probabilities. By incorporating local context into calibration, Adaptive Calibration corrects for a fundamental mismatch in cosine similarity, whereby...","url_abs":"https://arxiv.org/abs/2606.04469","url_pdf":"https://arxiv.org/pdf/2606.04469v1","authors":"[\"Ryan Brown\",\"Chris Russell\"]","published":"2026-06-03T05:29:44Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false}