%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{S.Luevano_IJCB2026_2026,
                      author = {S. Luevano, Luis and Ozturk, Unsal and Otroshi Shahreza, Hatef and George, Anjith and Marcel, S{\'{e}}bastien},
                    projects = {CARMEN, POPEYE},
         mainresearchprogram = {Human-AI Teaming},
  additionalresearchprograms = {AI for Everyone},
                       month = sep,
                       title = {Improving Low-Resolution Face Recognition under Limited Data: How Synthetic Data Generation Can Close the Domain Gap},
                   booktitle = {2026 IEEE International Joint Conference on Biometrics (IJCB)},
                        year = {2026},
                       pages = {IEEE},
                    location = {Rome, Italy},
                        note = {Focus Session "Generative AI for Fair and Secure Biometrics under Limited Data"},
                    abstract = {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 abun dant, 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.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2026/S.Luevano_IJCB2026_2026.pdf}
}