Paper Info
Title
Retinal Biometrics Based On Iterative Closest Point Algorithm
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%.
Year
DOI
Venue
2017
10.1109/EMBC.2017.8036840
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Computer vision,Computer science,Algorithm,Retinal image,3D projection,Jaccard index,Artificial intelligence,Retinal,Biometrics,Retinal blood vessels,Iterative closest point
Conference
2017
ISSN
Citations 
PageRank 
1094-687X
0
0.34
References 
Authors
7
8
Name
Order
Citations
PageRank
Y Hatanaka127624.77
Mikiya Tajima200.68
Kawasaki Ryo3359.17
Koko Saito400.34
Kazunori Ogohara501.69
Chisako Muramatsu631735.56
Wataru Sunayama75315.81
Hiroshi Fujita811824.65