  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.
