Stratified SIFT Matching for Human Iris Recognition

TL;DR

This paper introduces a three-stage stratified SIFT matching method for iris recognition that effectively filters out incorrect matches, significantly improving accuracy and reducing false acceptance rates.

Contribution

The paper presents a novel three-fold stratified SIFT matching approach that enhances iris recognition accuracy by filtering false matches using gradient and scaling analysis.

Findings

Achieves 96.08% accuracy on CASIA V3 database.

Achieves 97.15% accuracy on BATH database.

Significantly improves accuracy and FAR over existing SIFT methods.

Abstract

This paper proposes an efficient three fold stratified SIFT matching for iris recognition. The objective is to filter wrongly paired conventional SIFT matches. In Strata I, the keypoints from gallery and probe iris images are paired using traditional SIFT approach. Due to high image similarity at different regions of iris there may be some impairments. These are detected and filtered by finding gradient of paired keypoints in Strata II. Further, the scaling factor of paired keypoints is used to remove impairments in Strata III. The pairs retained after Strata III are likely to be potential matches for iris recognition. The proposed system performs with an accuracy of 96.08% and 97.15% on publicly available CASIAV3 and BATH databases respectively. This marks significant improvement of accuracy and FAR over the existing SIFT matching for iris.