发表机构
Idiap Research Institute（伊迪亚普研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对有限数据下的低分辨率人脸识别问题，研究多种合成数据生成策略，发现合成与真实数据存在域差距，简单插值增强紧凑骨干网络的合成方式效果最优，且生成方法需在真实LR数据上验证。
AI 中文摘要
监控场景中的人脸识别（FR）系统常遇到低分辨率（LR）人脸，其人脸区域低于标准112×112输入尺寸。虽然标注的高分辨率（HR）训练数据充足，但标注的原生LR数据，尤其是配对的原生LR/HR数据十分稀缺。一种解决方案是从可用的HR人脸合成LR数据，但合成工作量能在多大程度上提升识别精度仍不明确。本文针对面向边缘设备的紧凑人脸识别系统，研究了简单的合成生成策略，涵盖基于插值的降质、知识蒸馏、前置域Transformer（PDT）、Real ESRGAN式降质，以及带有身份感知损失的学习型超分辨率（SR）前端。我们在合成跨分辨率人脸基准数据集（LFW、CFP-FP、AgeDB-30）和真实原生LR数据集TinyFace上评估这些策略，发现了合成-真实差距：在合成基准上最优的降质设置，并非在真实LR上最优的设置。我们还发现，更多合成工作量不会带来单调提升：学习型SR前端的表现未超过将对齐后的LR图像直接输入强骨干网络的效果，而对紧凑骨干网络进行简单插值增强是唯一能优于自身基线的合成方式。我们得出结论，低分辨率人脸识别的生成方法必须在真实LR数据上并与直接输入基线进行验证，且在this https URL发布了我们的流程。
英文摘要
Face Recognition (FR) systems in surveillance settings often encounter Low Resolution (LR) faces, those whose face region falls below the standard 112 $\times$ 112 input size. While labelled High Resolution (HR) training data is abundant, labelled native-LR data, and above all paired native LR/HR data, is scarce. One workaround is to synthesize LR data from the available HR faces, but how much synthesis effort is repaid in recognition accuracy remains unclear. We present a study of simple synthetic generation strategies for a compact, edge device-oriented face recognition system, spanning interpolation-based degradation, knowledge distillation, a Prepended Domain Transformer (PDT), Real ESRGAN-style degradation, and a learned Super Resolution (SR) front-end with an identity-aware loss. We evaluate these strategies on synthetic cross-resolution face benchmarks (LFW, CFP-FP, AgeDB-30) and on TinyFace, a real-world native LR dataset, and expose a synthetic-real gap: the degradation setting that is optimal on synthetic benchmarks is not the one that is optimal on real LR. We find that more synthesis effort does not help monotonically: the learned SR front-end does not surpass a direct feed of the aligned LR image into a strong backbone, while simple interpolation augmentation of a compact backbone is the only synthesis that improves over its own baseline. We conclude that generative methods for LR face recognition must be validated on real LR and against a direct-feed baseline, and release our pipeline at https://idiap.ch/paper/synth-lrfr
CommentsAccepted at IEEE International Joint Conference on Biometrics (IJCB) 2026, Focus Session on Generative AI for Fair and Secure Biometrics under Limited Data
