Smartphone-based iris recognition through high-quality visible-spectrum iris image capture.V2

• URL: http://arxiv.org/abs/2510.06170v2
• Date: Mon, 27 Oct 2025 16:44:15 GMT
• Title: Smartphone-based iris recognition through high-quality visible-spectrum iris image capture.V2
• Authors: Naveenkumar G Venkataswamy, Yu Liu, Soumyabrata Dey, Stephanie Schuckers, Masudul H Imtiaz,
• Abstract summary: This work presents a compact end-to-end pipeline that enforces ISO/IEC 29794-6 quality compliance at acquisition.<n>Using a custom Android application, we introduce the CUVIRIS dataset of 752 compliant images from 47 subjects.<n>A lightweight MobileNetV3-based multi-task segmentation network (LightIrisNet) is developed for efficient on-device processing.
• Score: 3.713852186536256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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