Smartphone-based Iris Recognition through High-Quality Visible Spectrum Iris Capture
This work addresses the challenge of biometric authentication for smartphone users by enabling reliable iris recognition with accessible hardware, though it is incremental as it builds on existing VIS imaging methods.
This study tackled the problem of iris recognition using smartphone-captured visible spectrum (VIS) images and cross-spectral matching with near-infrared (NIR) images by developing a novel Android application for high-quality capture, achieving True Acceptance Rates of 96.57% for VIS and 97.95% for NIR images.
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.