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SegDenseNet: Iris Segmentation for Pre-and-Post Cataract Surgery
A. Lakra, P. Tripathi, R. Keshari, ,
Published in Institute of Electrical and Electronics Engineers Inc.
Volume: 2018-August
Pages: 3150 - 3155
Cataract is one of the major ophthalmic diseases worldwide which can potentially affect the performance of iris-based biometric systems. While existing research has shown that cataract does not have a major impact on iris recognition, our observations suggest that iris segmentation algorithms are not well equipped to handle cataract or post cataract surgery cases, thereby affecting the overall iris 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 DenseNet. The experiments on the IIITD Cataract Surgery Database show that improving iris segmentation enhances the recognition performance by up to 25% across different sensors and matchers. © 2018 IEEE.
About the journal
JournalData powered by TypesetProceedings - International Conference on Pattern Recognition
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.