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Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

Aleem, S, Sheng, B, Li, P, Yang, P and Feng, DD (2018) Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks. IEEE Transactions on Industrial Informatics. ISSN 1551-3203

Fast and Accurate Retinal Identification System Using Retinal Blood Vasculature Landmarks.pdf - Accepted Version

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IEEE The expansion of automation techniques and increased risk of identity theft have led emphasis on the tremendous need of automated identification system. Due to the high recognition accuracy and robustness to changes in human physiology, retinal biometric identification system has drawn much attention in this research field. In this paper, we aim to propose an automatic fast and accurate retinal identification system for the multi-sample data set. The proposed approach uses a hybrid segmentation technique to segment out both thick/thin vessels for effectively balancing the difference of wavelet response between thick/thin blood vessels. As a result, recognition accuracy is improved. A PCA (Principle Component Analysis) based feature processing approach is proposed for efficiently reducing the dimensionality of a large number of vessels features. It significantly reduces computation time and accelerates the matching process in the retinal identification system. The proposed technique is validated on DRIVE, STARE, VARIA, RIDB, HRF, Messidor, DIARETDB0, and a large multi-sample per subject database created by authors using the images provided by Dr. Chen (Shanghai Jiao Tong University Affiliated Sixth People Hospital). Experimental results demonstrated that the proposed approach outperforms other existing techniques. Segmentation achieves an overall accuracy of 99.65% with the recognition rate of 99.40% on all these databases.

Item Type: Article
Additional Information: © 2018 IEEE
Uncontrolled Keywords: 08 Information and Computing Sciences, 09 Engineering, 10 Technology
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Divisions: Computer Science & Mathematics
Publisher: IEEE
Date Deposited: 07 Feb 2019 11:52
Last Modified: 04 Sep 2021 09:47
DOI or ID number: 10.1109/TII.2018.2881343
URI: https://researchonline.ljmu.ac.uk/id/eprint/10008
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