{"ID":2828097,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15548","arxiv_id":"2512.15548","title":"An Open-Source Framework for Quality-Assured Smartphone-Based Visible Light Iris Recognition","abstract":"Smartphone-based iris recognition in the visible spectrum (VIS) offers a low-cost and accessible biometric alternative but remains a challenge due to lighting variability, pigmentation effects, and the limited adoption of standardized capture protocols. In this work, we present CUVIRIS, a dataset of 752 ISO/IEC 29794-6 compliant iris images from 47 subjects, collected with a custom Android application that enforces real-time framing, sharpness assessment, and quality feedback. We further introduce LightIrisNet, a MobileNetV3-based multi-task segmentation model optimized for on-device deployment. In addition, we adapt IrisFormer, a transformer-based matcher, to the VIS domain. We evaluate OSIRIS and IrisFormer under a standardized protocol and benchmark against published CNN baselines reported in prior work. On CUVIRIS, the open-source OSIRIS system achieves a TAR of 97.9% at FAR = 0.01 (EER = 0.76%), while IrisFormer, trained only on the UBIRIS.v2 dataset, achieves an EER of 0.057\\%. To support reproducibility, we release the Android application, LightIrisNet, trained IrisFormer weights, and a subset of the CUVIRIS dataset. These results show that, with standardized acquisition and VIS-adapted lightweight models, accurate iris recognition on commodity smartphones is feasible under controlled conditions, bringing this modality closer to practical deployment.","short_abstract":"Smartphone-based iris recognition in the visible spectrum (VIS) offers a low-cost and accessible biometric alternative but remains a challenge due to lighting variability, pigmentation effects, and the limited adoption of standardized capture protocols. In this work, we present CUVIRIS, a dataset of 752 ISO/IEC 29794-6...","url_abs":"https://arxiv.org/abs/2512.15548","url_pdf":"https://arxiv.org/pdf/2512.15548v1","authors":"[\"Naveenkumar G. Venkataswamy\",\"Yu Liu\",\"Soumyabrata Dey\",\"Stephanie Schuckers\",\"Masudul H. Imtiaz\"]","published":"2025-12-17T15:55:04Z","proceeding":"eess.IV","tasks":"[\"eess.IV\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}