Retina verification using a combined points and edges approach

Ee Ping Ong, Yanwu Xu, Damon Wing Kee Wong, Jiang Liu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages2720-2724
Number of pages5
ISBN (Electronic)9781479983391
DOIs
Publication statusPublished - 9 Dec 2015
Externally publishedYes
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 27 Sep 201530 Sep 2015

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December
ISSN (Print)1522-4880

Conference

ConferenceIEEE International Conference on Image Processing, ICIP 2015
Country/TerritoryCanada
CityQuebec City
Period27/09/1530/09/15

Keywords

  • Least-Median-Squares estimator
  • biometric
  • edge dissimilarity measure
  • feature points' matching
  • robustified Hausdorff distance

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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