A New Approach for Automatic Human Recognition Based on Retinal Blood Vessels Analysis

Retinal biometric is one of the newest biometric technologies that increasingly being used, especially in critical areas that require tight security measures, in order that retinal is one of the most robust and most accurate biometrics methods to recognize a person, the retinal recognition technique is yet another step in biometrics, it deals with very distinct physical property of exceptionally very low false acceptance and false rejection rates, and features that are found in the retina of eye are more reliable and stable features than those found in other biometrics. Retinal biometric is one of the newest biometric technologies that increasingly being used, especially in critical areas that require tight security measures, in order that retinal is one of the most robust and most accurate biometrics methods to recognize a person, the retinal recognition technique is yet another step in biometrics, it deals with very distinct physical property of exceptionally very low false acceptance and false rejection rates, and features that are found in the retina of eye are more reliable and stable features than those found in other biometrics.

This paper presents a new system for personal recognition based on retinal vascular pattern. This system is insensitive to rotation and robust to noise and brightness variations. The presented system consists of three main stages (i.e., preprocessing, feature extraction, and matching stage). Preprocessing is used for enhancement and segmentation the vascular network (i.e., Region of Interest), as discriminating feature the set of local average of vascular densities have been used in feature extraction stage, finally Euclidean distance measure used in matching stage. The proposed system is evaluated on the two publicly available databases: (i) STARE (Structured Analysis of the Retina) and (ii) DRIVE (Digital Retinal Images for Vessel Extraction). The test results indicated that the attained recognition accuracy of the proposed method is 100% for both datasets.