Improving Low-Resolution Face Recognition under Limited Data: How Synthetic Data Generation Can Close the Domain Gap

Face Recognition (FR) systems in surveillance settings often encounter Low Resolution (LR) faces, those whose face region falls below the standard 112 × 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.

Our main conclusions are:

• The degradation setting that is optimal on synthetic benchmarks is not the one that is optimal on real LR: 28 ↓c/↑a is best across the synthetic benchmarks yet is the worst configuration on TinyFace, where the milder 56 px setting wins.
• Synthesis effort does not pay off monotonically; on a retrainable compact backbone the cheapest interpolation augmentation beats both Real-ESRGAN-style degradation and a learned SR front-end.
• A learned, identity-aware super-resolution front-end never beats simply feeding a strong frozen backbone the aligned LR image, so a direct-feed baseline should be reported before any restoration or translation pipeline is claimed to help.
• The average-accuracy gains from LR-aware synthesis do not reduce demographic bias: the FMR disparity on RFW shows no consistent improvement.

Accepted at the Focus Session on Generative AI for Fair and Secure Biometrics under Limited Data at IJCB 2026. Camera-ready and project page coming soon.

Project page and pipeline (coming soon)

Latex citation:

@inproceedings{luevano2026synthlrfr,
  title={Improving Low-Resolution Face Recognition under Limited Data: How Synthetic Data Generation Can Close the Domain Gap},
  author={Luevano, Luis S. and {\"O}zt{\"u}rk, {\"U}nsal and Otroshi Shahreza, Hatef and George, Anjith and Marcel, S{\'e}bastien},
  booktitle={2026 IEEE International Joint Conference on Biometrics (IJCB)},
  year={2026},
  note={Accepted at the 2026 IJCB Focus Session "Generative AI for Fair and Secure Biometrics under Limited Data"}
}