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 often improves worst-group ranking and probability quality across a variety of pretrained models and standard benchmarks, with gains in low-FPR verification performance depending on the setting. AC provides a practical solution for equitable facial recognition, without requiring demographic group annotations, and while improving overall performance. Our method provides continuous, region-specific calibration, and we examine per-group performance to assess "leveling down" where fairness comes at the cost of degraded performance for some groups.

36 pages, 2 figures. Revised implementation details and discussion; added component ablations, LFW validation, and robustness analyses

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2606.04469 [cs.CV]

(or arXiv:2606.04469v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.04469 Focus to learn more arXiv-issued DOI via DataCite

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