Retina verification using a combined points and edges approach

Retina verification using a combined points and edges approach
Title:
Retina verification using a combined points and edges approach
Other Titles:
2015 IEEE International Conference on Image Processing (ICIP)
DOI:
10.1109/ICIP.2015.7351297
Keywords:
Publication Date:
27 September 2015
Citation:
E. P. Ong, Y. Xu, D. W. K. Wong and J. Liu, "Retina verification using a combined points and edges approach," Image Processing (ICIP), 2015 IEEE International Conference on, Quebec City, QC, 2015, pp. 2720-2724. doi: 10.1109/ICIP.2015.7351297
Abstract:
This paper presents a novel retina biometric scheme that performs person verification based on passing 2 stages: robust feature points matching and edge dissimilarity measure. Our approach differs from those in the literature as we propose the use of edges and edge dissimilarity measure for retina verification. Our first-stage matching/authentication utilizes robust feature points' matching to determine tentatively whether there is a “match” and if so, performs image registration between the test and template retina image. The robust feature points' matching is achieved in 2 steps: graph-based feature points' matching followed by pruning of wrongly matched feature points using a Least-Median-Squares estimator that enforces an affine transformation geometric constraint. To compute edge dissimilarity measure in our second-stage matching/authentication, we propose the “robustified Hausdorff distance”. We show that our proposed approach outperforms two of the state-of-the-art approaches when tested on the same dataset.
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PublisherCopyrights
Funding Info:
Description:
(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
978-1-4799-8338-4
ISBN:
978-1-4799-8339-1
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