用合成数据提升低分辨率人脸识别，发现简单方法比复杂生成更有效。
Improving Low-Resolution Face Recognition under Limited Data: How Synthetic Data Generation Can Close the Domain Gap
- 用插值、SR等方法生成低分辨率人脸数据，对比不同策略效果。
- 真实低分辨数据上，简单插值增强反而优于复杂生成模型。
- 建议在真实低分辨数据上验证生成方法，避免过度依赖合成基准。
监控场景中的面部识别系统常需处理低于标准输入尺寸112×112的低分辨率（LR）人脸。尽管高分辨率（HR）标注数据丰富，但原生低分辨率数据，尤其是配对的低/高分辨率数据却极为稀缺。一种常见做法是从可用的高分辨率图像中合成低分辨率数据，但合成投入与识别准确率之间的回报关系尚不明确。本文研究了适用于边缘设备的小型化人脸识别系统中几种简单的合成生成策略，包括基于插值的退化、知识蒸馏、前置域变换器（PDT）、类似Real ESRGAN的退化方式，以及带有身份感知损失的可学习超分辨率（SR）前端。我们在合成跨分辨率基准（LFW、CFP-FP、AgeDB-30）和真实低分辨率数据集TinyFace上评估这些策略，揭示出合成-真实差距：在合成基准上表现最优的退化方式，在真实低分辨率数据上并非最优。我们发现，合成努力并非越多越好：可学习的超分辨率前端并未超越直接将对齐后的低分辨率图像输入强骨干网络的效果；而唯一显著提升基线性能的合成方法，是紧凑骨干网络的简单插值增强。结论是，面向低分辨率人脸识别的生成方法必须在真实低分辨率数据上验证，并与直接输入基线进行对比。相关代码与流程已公开于https://idiap.ch/paper/synth-lrfr。
原文摘要 · Abstract (English)
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
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