Smartphone-based Iris Recognition through High-Quality Visible Spectrum Iris Capture

TL;DR

This paper presents a novel smartphone application that captures high-quality visible spectrum iris images and performs cross-spectral recognition with NIR images, achieving high accuracy and robustness for biometric authentication.

Contribution

The study introduces an Android app with automated focus, iris detection, and segmentation, enabling effective cross-spectral iris recognition using smartphone cameras, which was previously underexplored.

Findings

Achieved 96.57% TAR for VIS images and 97.95% for NIR images.

Validated robustness across different distances and iris colors.

Demonstrated potential for smartphone-based biometric security.

Abstract

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.