A Step Forward Towards Trustworthy Risk-Aware Facial Retrieval (RA-FR)

• URL: http://arxiv.org/abs/2607.16279v1
• Date: Thu, 09 Jul 2026 14:55:45 GMT
• Title: A Step Forward Towards Trustworthy Risk-Aware Facial Retrieval (RA-FR)
• Abstract summary: Risk-Aware Facial Retrieval (RA-FR) is a framework that moves beyond fixed Top-$k$ retrieval to adaptive set generation.<n> RA-FR consistently satisfies a 5% risk target with an average retrieval set size of approximately 10 images.
• Score: 0.6445605125467574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial image retrieval in unconstrained surveillance environments is a high-stakes challenge where missing a subject of interest -- a single false negative -- is simply not an option. Despite near-perfect performance on curated benchmarks, current recognition systems falter under real-world domain shifts such as low resolution, motion blur, and uncontrolled illumination (e.g., SCFace). Addressing this reliability gap, we propose Risk-Aware Facial Retrieval (RA-FR), a framework that moves beyond fixed Top-$k$ retrieval to adaptive set generation, guaranteeing ground truth inclusion within a user-specified risk level ($α$) and confidence level ($1 - δ$). Our approach integrates three core contributions: (1) reducing aleatoric uncertainty via a hybrid blind face restoration technique coupling Latent Consistency Models (InterLCM) and DiffBIR; (2) extracting discriminative, restoration-robust features via self-supervised DINOv1 ViT-B with GGeM pooling; and (3) employing conformal prediction with Hoeffding's inequality to dynamically calibrate retrieval set sizes based on query uncertainty. On the IMFDB benchmark, it consistently satisfies a 5% risk target with an average retrieval set size of approximately 10 images. By unifying domain-specific restoration, robust representation learning, and provable decision rules, RA-FR offers a pipeline that makes facial retrieval in surveillance both reliable and auditable. The code is available at: https://github.com/MuhammadEmmadSiddiqui/RA-FR.

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