Human Recognition based on Retinal Bifurcations and Modified Correlation Function

TL;DR

This paper proposes a novel retinal recognition method using bifurcation points and a modified correlation function, achieving high accuracy for secure identification.

Contribution

It introduces a new mathematical approach for retinal feature extraction and recognition, enhancing security systems with improved accuracy.

Findings

Achieved 99.34% recognition accuracy

Utilized new mathematical functions on retinal bifurcations

Validated on multiple retinal image databases

Abstract

Nowadays high security is an important issue for most of the secure places and recent advances increase the needs of high-security systems. Therefore, needs to high security for controlling and permitting the allowable people to enter the high secure places, increases and extends the use of conventional recognition methods. Therefore, a novel identification method using retinal images is proposed in this paper. For this purpose, new mathematical functions are applied on corners and bifurcations. To evaluate the proposed method we use 40 retinal images from the DRIVE database, 20 normal retinal image from STARE database and 140 normal retinal images from local collected database and the accuracy rate is 99.34 percent.