用手机拍高清虹膜，跨光谱识别准确率超96%。
Smartphone-based Iris Recognition through High-Quality Visible Spectrum Iris Capture
- 自动对焦变焦+AI检测分割，提升手机拍虹膜质量。
- 可见光虹膜识别正确率达96.57%，近红外达97.95%。
- 适配多种距离和虹膜颜色，适合移动端生物识别应用。
虹膜识别以高精度著称，传统依赖近红外（NIR）成像。近年，利用智能手机摄像头拍摄可见光（VIS）虹膜图像成为新方向。然而，关于使用手机捕捉高质量VIS虹膜图像，并与已注册的NIR图像进行跨光谱匹配的研究仍不充分。主要挑战在于手机摄像头难以获取高质量生物特征。本研究设计了一款新型Android应用，通过自动对焦和缩放调节，稳定捕获高质量VIS虹膜图像。该应用集成YOLOv3-tiny模型实现精准眼区与虹膜定位，采用轻量级Ghost-Attention U-Net（G-ATTU-Net）完成分割，符合ISO/IEC 29794-6图像质量标准。在47名受试者的数据上验证，手机采集的VIS虹膜识别真接受率（TAR）达96.57%，NIR图像达97.95%，在不同拍摄距离和虹膜颜色下均表现稳定。该方案有望显著推动虹膜生物识别发展，增强手机安全能力。
原文摘要 · Abstract (English)
Iris recognition is widely acknowledged for its exceptional accuracy in biometric authentication, traditionally relying on near-infrared (NIR) imaging. Recently, visible spectrum (VIS) imaging via accessible smartphone cameras has been explored for biometric capture. However, a thorough study of iris recognition using smartphone-captured 'High-Quality' VIS images and cross-spectral matching with previously enrolled NIR images has not been conducted. The primary challenge lies in capturing high-quality biometrics, a known limitation of smartphone cameras. This study introduces a novel Android application designed to consistently capture high-quality VIS iris images through automated focus and zoom adjustments. The application integrates a YOLOv3-tiny model for precise eye and iris detection and a lightweight Ghost-Attention U-Net (G-ATTU-Net) for segmentation, while adhering to ISO/IEC 29794-6 standards for image quality. The approach was validated using smartphone-captured VIS and NIR iris images from 47 subjects, achieving a True Acceptance Rate (TAR) of 96.57% for VIS images and 97.95% for NIR images, with consistent performance across various capture distances and iris colors. This robust solution is expected to significantly advance the field of iris biometrics, with important implications for enhancing smartphone security.
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