SegDenseNet: Iris Segmentation for Pre and Post Cataract Surgery
This addresses a domain-specific issue for biometric systems in regions like India with high cataract surgery rates, but it is incremental as it adapts an existing deep learning method to a new dataset.
The paper tackles the problem of iris segmentation failing in cataract and post-surgery cases, which degrades biometric recognition, and shows that the proposed SegDenseNet algorithm improves identification by up to 25% on a cataract database.
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.