A retinal image authentication framework based on a graph-based representation algorithm in a two-stage matching structure

Retinal vascular pattern has many valuable characteristics such as uniqueness, stability and permanence as a basis for human authentication in security applications. This paper presents an automatic rotation-invariant retinal authentication framework based on a novel graph-based retinal representation scheme. In the proposed framework, to replace the retinal image with a relational mathematical graph (RMG), we propose a novel RMG definition algorithm from the corresponding blood vessel pattern of the retinal image. Then, the unique features of RMG are extracted to supplement the authentication process in our framework. The authentication process is carried out in a two-stage matching structure. In the first stage of this scenario, the defined RMG of enquiry image is authenticated with enrolled RMGs in the database based on isomorphism theory. If the defined RMG of enquiry image is not isomorphic with none enrolled RMG in the database, in the second stage of our matching structure, the authentication is performed based on the extracted features from the defined RMG by a similarity-based matching scheme. The proposed graph-based authentication framework is evaluated on VARIA database and accuracy rate of 97.14% with false accept ratio of zero and false reject ratio of 2.85% are obtained. The experimental results show that the proposed authentication framework provides the rotation invariant, multi resolution and optimized features with low computational complexity for the retina-based authentication application.