A personal identification system using retinal vasculature in retinal fundus images

KÖSE C., Ikibas C.

EXPERT SYSTEMS WITH APPLICATIONS, vol.38, no.11, pp.13670-13681, 2011 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 11
  • Publication Date: 2011
  • Doi Number: 10.1016/j.eswa.2011.04.141
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.13670-13681
  • Keywords: Biometric identification, Retinal identification, Retinal image analysis, Vessel segmentation, Similarity measurement, MACULAR DEGENERATION, BLOOD-VESSELS, SEGMENTATION, DIAGNOSIS
  • Karadeniz Technical University Affiliated: Yes


The characteristics of human body such as fingerprint, face, hand palm and iris are measured, recorded and identified by performing comparison using biometric devices. Even though it has not seen widespread acceptance yet, retinal identification based on retinal vasculatures in retina provides the most secure and accurate authentication means among biometric systems. Using retinal images taken from individuals, retinal identification is employed in environments such as nuclear research centers and facilities, weapon factories, where extremely high security measures are needed. The superiority of this method stems from the fact that retina is unique to every human being and it would not be changed during human life. Adversely, other identification approaches such as fingerprint, face, palm and iris recognition, are all vulnerable in that those characteristics can be corrupted via plastic surgeries and other changes. In this study we propose an alternate personal identification system based on retinal vascular network in retinal images, which tolerates scale, rotation and translation in comparison. In order to accurately identify a person our new approach first segments vessel structure and then employ similarity measurement along with the tolerations. The developed system, tested on about four hundred images, presents over 95% of success which is quite promising. (C) 2011 Elsevier Ltd. All rights reserved.