Iris Recognition with a Database of Iris Images Obtained in Visible Light Using Smartphone Camera

TL;DR

This study introduces a new publicly available database of iris images captured in visible light with a smartphone camera, demonstrating high recognition accuracy with existing iris recognition methods and highlighting segmentation as a key challenge.

Contribution

The paper presents the first high-quality visible-light iris image database from a mobile device and evaluates existing recognition methods on this data, showing promising results.

Findings

High recognition accuracy (>94.5%) across methods

Zero or near-zero failure to enroll rate

Segmentation errors significantly impact accuracy

Abstract

This paper delivers a new database of iris images collected in visible light using a mobile phone's camera and presents results of experiments involving existing commercial and open-source iris recognition methods, namely: IriCore, VeriEye, MIRLIN and OSIRIS. Several important observations are made. First, we manage to show that after simple preprocessing, such images offer good visibility of iris texture even in heavily-pigmented irides. Second, for all four methods, the enrollment stage is not much affected by the fact that different type of data is used as input. This translates to zero or close-to-zero Failure To Enroll, i.e., cases when templates could not be extracted from the samples. Third, we achieved good matching accuracy, with correct genuine match rate exceeding 94.5% for all four methods, while simultaneously being able to maintain zero false match rate in every case. Correct genuine match rate of over 99.5% was achieved using one of the commercial methods, showing that such images can be used with the existing biometric solutions with minimum additional effort required. Finally, the experiments revealed that incorrect image segmentation is the most prevalent cause of recognition accuracy decrease. To our best knowledge, this is the first database of iris images captured using a mobile device, in which image quality exceeds this of a near-infrared illuminated iris images, as defined in ISO/IEC 19794-6 and 29794-6 documents. This database will be publicly available to all researchers.