Segmentation-Aware and Adaptive Iris Recognition

TL;DR

This paper introduces a segmentation-aware, adaptive iris recognition framework that leverages periocular information and importance-weighted similarity measures to improve accuracy in less-constrained imaging conditions.

Contribution

It proposes a novel adaptive framework that dynamically incorporates periocular data and importance weighting to enhance iris recognition accuracy under challenging scenarios.

Findings

Improved recognition accuracy in less-constrained environments.

Effective use of periocular information to compensate for degraded iris regions.

Validated performance gains on multiple public iris datasets.

Abstract

Iris recognition has emerged as one of the most accurate and convenient biometric for the human identification and has been increasingly employed in a wide range of e-security applications. The quality of iris images acquired at-a-distance or under less constrained imaging environments is known to degrade the iris matching accuracy. The periocular information is inherently embedded in such iris images and can be exploited to assist in the iris recognition under such non-ideal scenarios. Our analysis of such iris templates also indicates significant degradation and reduction in the region of interest, where the iris recognition can benefit from a similarity distance that can consider importance of different binary bits, instead of the direct use of Hamming distance in the literature. Periocular information can be dynamically reinforced, by incorporating the differences in the effective area of available iris regions, for more accurate iris recognition. This paper presents such a segmentation-assisted adaptive framework for more accurate less-constrained iris recognition. The effectiveness of this framework is evaluated on three publicly available iris databases using within-dataset and cross-dataset performance evaluation and validates the merit of the proposed iris recognition framework.