Adaptive Calibration for Fair and Performant Facial Recognition
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...
| Authors | Ryan Brown, Chris Russell |
|---|---|
| Primary category | cs.CV |
| Published | 3 June 2026 |
| Last updated at source | 8 September 2026 |
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