In this paper the design of hybrid retina matching algorithm that is used in identification systems is considered. Retina based recognition is apparent as the most secure method for identification of an identity utilized to differentiate persons.

     The characteristics of Speeded up Robust Feature (SURF) and Binary Robust Invariant Scalable Key-Points (BRISK) algorithm have been used in order to produce a fast matching algorithm than the classical ones, those characteristics are important for real-time applications which usually need quick processing of a growing quantity of data. The algorithm is divided into three stages: retinal image processing and segmentation, extracting the local features from the retinal images using SURF and BRISK descriptors and finally matching those features using KNN matcher. The algorithm was trained with structured analysis of retina (STARE) database giving accuracy that reach 100%.