SegDenseNet: Iris Segmentation for Pre and Post Cataract Surgery

TL;DR

This paper introduces SegDenseNet, a deep learning-based iris segmentation method designed to improve recognition accuracy in cataract and post-surgery cases, addressing a gap in existing segmentation approaches.

Contribution

The paper presents a novel DenseNet-based iris segmentation algorithm tailored for cataract-affected eyes, enhancing recognition performance in challenging biometric scenarios.

Findings

SegDenseNet improves iris recognition accuracy by up to 25%.

The algorithm effectively handles cataract and post-surgery variations.

Experiments on IIITD Cataract database validate the approach.

Abstract

Cataract is caused due to various factors such as age, trauma, genetics, smoking and substance consumption, and radiation. It is one of the major common ophthalmic diseases worldwide which can potentially affect iris-based biometric systems. India, which hosts the largest biometrics project in the world, has about 8 million people undergoing cataract surgery annually. While existing research shows that cataract does not have a major impact on iris recognition, our observations suggest that the iris segmentation approaches are not well equipped to handle cataract or post cataract surgery cases. Therefore, failure in iris segmentation affects the overall recognition performance. This paper presents an efficient iris segmentation algorithm with variations due to cataract and post cataract surgery. The proposed algorithm, termed as SegDenseNet, is a deep learning algorithm based on DenseNets. The experiments on the IIITD Cataract database show that improving the segmentation enhances the identification by up to 25% across different sensors and matchers.