Experimental analysis regarding the influence of iris segmentation on the recognition rate

TL;DR

This paper investigates how iris segmentation accuracy and parameter choices affect the overall performance of iris biometric systems, highlighting the importance of precise segmentation for reliable recognition.

Contribution

It systematically evaluates the impact of segmentation accuracy and parameter selection on iris recognition performance, providing insights into their significance.

Findings

Segmentation accuracy correlates with recognition performance.

Parameter choices significantly influence the biometric system's effectiveness.

Consistency in segmentation improves overall recognition results.

Abstract

In this study the authors will look at the detection and segmentation of the iris and its influence on the overall performance of the iris-biometric tool chain. The authors will examine whether the segmentation accuracy, based on conformance with a ground truth, can serve as a predictor for the overall performance of the iris-biometric tool chain. That is: If the segmentation accuracy is improved will this always improve the overall performance? Furthermore, the authors will systematically evaluate the influence of segmentation parameters, pupillary and limbic boundary and normalisation centre (based on Daugman's rubbersheet model), on the rest of the iris-biometric tool chain. The authors will investigate if accurately finding these parameters is important and how consistency, that is, extracting the same exact region of the iris during segmenting, influences the overall performance.