发表机构
National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对无约束监控环境下面部检索问题，提出RA - FR框架，通过结合混合盲脸恢复技术、自监督特征提取和共形预测，实现自适应集生成，在IMFDB基准测试中满足5%风险目标，使面部检索可靠且可审计。
AI 中文摘要
在无约束监控环境中的面部图像检索是一项高风险挑战，错过感兴趣的对象是不可接受的。尽管在精心策划的基准测试中表现近乎完美，但当前识别系统在低分辨率、运动模糊和光照不受控制等现实世界域转移情况下表现不佳。为解决这一可靠性差距，我们提出风险感知面部检索（RA - FR）框架，它超越固定的Top - k检索，转向自适应集生成，保证在用户指定的风险水平（$\alpha$）和置信水平（$1 - \delta$）内包含真实情况。我们的方法整合了三个核心贡献：通过结合潜在一致性模型（InterLCM）和DiffBIR的混合盲脸恢复技术降低偶然不确定性；通过带有GGeM池化的自监督DINOv1 ViT - B提取有判别力、恢复鲁棒的特征；采用基于霍夫丁不等式的共形预测根据查询不确定性动态校准检索集大小。在IMFDB基准测试中，它始终满足5%的风险目标，平均检索集大小约为10张图像。通过统一特定领域的恢复、鲁棒表示学习和可证明的决策规则，RA - FR提供了一个使监控中的面部检索既可靠又可审计的管道。代码可在指定网址获取。
英文摘要
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.
CommentsAccepted at the 28th International Conference on Pattern Recognition (ICPR 2026). Extended version with additional citations and expanded sections
