Pigment Melanin: Pattern for Iris Recognition

TL;DR

This paper explores using melanin pigment patterns in visible light images for iris recognition, combining features from VL and NIR images to improve accuracy, and introduces a new database for evaluation.

Contribution

It proposes a novel method leveraging melanin patterns in VL imaging and fuses VL and NIR features to enhance iris recognition performance.

Findings

Melanin patterns in VL images are highly distinctive for iris recognition.

Fusion of VL and NIR features improves recognition accuracy.

The proposed algorithm outperforms traditional methods.

Abstract

Recognition of iris based on Visible Light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, unavailable in Near-Infrared (NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive feature-code for each subject. An important question is how much the melanin patterns, extracted from VL, are independent of iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost the recognition performance. We have collected our own database (UTIRIS) consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of cromophores and improves the iris recognition rate.