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.

翻译：虹膜识别因其在生物特征认证中的卓越准确性而广受认可，传统上依赖于近红外（NIR）成像。近年来，人们开始探索通过普及的智能手机摄像头进行可见光谱（VIS）成像以捕获生物特征。然而，利用智能手机捕获的“高质量”VIS图像进行虹膜识别，并与先前注册的NIR图像进行跨光谱匹配，尚未得到深入研究。主要挑战在于捕获高质量的生物特征图像，这是智能手机摄像头的一个已知局限。本研究介绍了一款新颖的Android应用程序，旨在通过自动对焦和变焦调整，持续捕获高质量的VIS虹膜图像。该应用程序集成了YOLOv3-tiny模型用于精确的眼睛和虹膜检测，以及一个轻量级的Ghost-Attention U-Net（G-ATTU-Net）用于分割，同时遵循ISO/IEC 29794-6标准的图像质量要求。该方法使用来自47名受试者的智能手机捕获的VIS和NIR虹膜图像进行了验证，对于VIS图像实现了96.57%的真实接受率（TAR），对于NIR图像实现了97.95%的TAR，并且在不同的捕获距离和虹膜颜色下均表现出稳定的性能。这一鲁棒的解决方案有望显著推进虹膜生物识别领域的发展，并对增强智能手机安全性具有重要意义。