Retinal Biometrics based on Iterative Closest Point Algorithm

Yuji Hatanaka, Mikiya Tajima, Ryo Kawasaki1, Koko Saito2, Kazunori Ogohara, Chisako Muramatsu, Wataru Sunayama3, Hiroshi Fujita

  • 1Yamagata University
  • 2Shinoda General Hospital
  • 3The University of Shiga Prefecture

Details

15:20 - 15:35 | Wed 12 Jul | Rushmer Room | WeBT16.5

Session: Retinal Imaging I

Abstract

The pattern of blood vessels in the eye is unique to each person because it rarely changes over time. Therefore, it is well known that retinal blood vessels are useful for biometrics. This paper describes a biometrics method using the Jaccard similarity coefficient (JSC) based on blood vessel regions in retinal image pairs. The retinal image pairs were rough matched by the center of their optic discs. Moreover, the image pairs were aligned using the Iterative Closest Point algorithm based on detailed blood vessel skeletons. For registration, perspective transform was applied to the retinal images. Finally, the pairs were classified as either correct or incorrect using the JSC of the blood vessel region in the image pairs. The proposed method was applied to temporal retinal images, which were obtained in 2009 (695 images) and 2013 (87 images). The 87 images acquired in 2013 were all from persons already examined in 2009. The accuracy of the proposed method reached 100%.