Abstract
Authentication reliability of individuals is a demanding service and growing in many areas, not only in the military barracks or police services but also in applications of community and civilian, such as financial transactions. In this paper, we propose a human verification method depends on extraction a set of retinal features points. Each set of feature points is representing landmarks in the tree of retinal vessel. Extraction and matching of the pattern based on Gabor filters and SVM are described. The validity of the proposed method is verified with experimental results obtained on three different commonly available databases, namely STARE, DRIVE and VARIA. We note that the proposed retinal verification method gives 92.6%, 100% and 98.2% recognition rates for the previous databases, respectively. Furthermore, for the authentication task, the proposed method gives a moderate accuracy of retinal vessel images from these databases.
Highlights
Accurate and reliable identification and verification play a significant role for systems of security and modern progress of reliable authoritative systems
The second database of retinal images database were used, namely VARIA database. It is a collection of retinal images applied for authentication objectives
We used the following performance measures in our analysis to determine the accuracy of proposed method, Equal Error Rate (EER), False Acceptance Rate (FAR), Genuine Acceptance Rate (GAR), and False Rejection Rate (FRR)
Summary
Accurate and reliable identification and verification play a significant role for systems of security and modern progress of reliable authoritative systems. Retina of human involves blood-vessels pattern, which is unique and characterize in each person and can be applied in a biometric system [3] [4]. The uniqueness and importance of blood vessels and its pattern in human retina were revealed by two eye specialists Carleton and Goldstein during researching on diseases which infect eye [9] They detected that each eye of individual has a single pattern of vascular that can be applied for authentication of persons. SVM classification and feature extraction with each other have an employ even when prediction of anonymous models is not needed They can be applied to distinguish key sets which are interested in any operation identify the classes [25].
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