arXiv:2606.04469v1 Announce Type: cross 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 appro
The increasing deployment of AI in sensitive applications like facial recognition necessitates continuous improvements in fairness and performance, driven by societal and regulatory demands.
This development addresses critical challenges in facial recognition by enhancing accuracy and mitigating bias without relying on explicit demographic data, which is crucial for ethical AI deployment.
Facial recognition systems can now achieve more equitable and reliable results through adaptive calibration, potentially reducing discriminatory outcomes and increasing public trust.
- · AI developers
- · Security sectors
- · Public sector agencies
- · Consumers concerned with fairness
- · Systems relying on biased legacy algorithms
- · Organizations prioritising speed over fairness
Facial recognition becomes more reliable and less prone to demographic bias within various applications.
Improved fairness could accelerate the adoption and integration of facial recognition into more public and private services.
Reduced bias in core AI components could lead to a broader demand for 'fairness-by-design' principles across the entire AI stack.
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Read at arXiv cs.AI