用手机拍虹膜也能高精度识别，靠的是标准化拍摄和轻量模型。
Smartphone-based iris recognition through high-quality visible-spectrum iris image capture.V2
- 用实时反馈的安卓应用保证拍摄质量，符合国际标准。
- 在自建数据集上，识别错误率低至0.057%，准确率达97.9%。
- 模型轻量适合手机运行，开源代码数据助力复现。
基于智能手机的可见光谱虹膜识别因光照变化、色素差异及缺乏标准化采集控制而困难重重。本文提出一个紧凑的端到端流程，在采集阶段强制符合ISO/IEC 29794-6质量标准，并验证了在消费级设备上实现高精度可见光虹膜识别的可行性。通过定制Android应用实现实时构图、清晰度评估与反馈，构建了包含47名受试者共752张合规图像的CUVIRIS数据集。开发了基于MobileNetV3的轻量级多任务分割网络（LightIrisNet）用于高效机载处理，并将Transformer匹配器（IrisFormer）适配至可见光域。在标准化协议下对比先前CNN基线，OSIRIS在FAR=0.01时达到TAR=97.9%（EER=0.76%），IrisFormer仅在UBIRIS.v2上训练，便在CUVIRIS上实现EER=0.057%。采集应用、训练模型及数据集公开子集均已发布，支持可复现性。结果表明，标准化采集与可见光适配的轻量模型可实现手机端精准且实用的虹膜识别。
原文摘要 · Abstract (English)
Smartphone-based iris recognition in the visible spectrum (VIS) remains difficult due to illumination variability, pigmentation differences, and the absence of standardized capture controls. This work presents a compact end-to-end pipeline that enforces ISO/IEC 29794-6 quality compliance at acquisition and demonstrates that accurate VIS iris recognition is feasible on commodity devices. Using a custom Android application performing real-time framing, sharpness evaluation, and feedback, we introduce the CUVIRIS dataset of 752 compliant images from 47 subjects. A lightweight MobileNetV3-based multi-task segmentation network (LightIrisNet) is developed for efficient on-device processing, and a transformer matcher (IrisFormer) is adapted to the VIS domain. Under a standardized protocol and comparative benchmarking against prior CNN baselines, OSIRIS attains a TAR of 97.9% at FAR=0.01 (EER=0.76%), while IrisFormer, trained only on UBIRIS.v2, achieves an EER of 0.057% on CUVIRIS. The acquisition app, trained models, and a public subset of the dataset are released to support reproducibility. These results confirm that standardized capture and VIS-adapted lightweight models enable accurate and practical iris recognition on smartphones.
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