Feature point validation for improved retina recognition

The retinal recognition technique is yet another step in biometrics, as a very rare but the most accurate authentication method. It contains the distinct property of exceptionally very low false acceptance and false rejection rates. The performance of any biometrics based identification system is enormously affected by its correct matching rate. In this paper, we present a new windowing technique for feature point validation in order to improve the accuracy of retinal vascular pattern recognition. The proposed method applies a windowing technique on skeleton vascular pattern to eliminate false feature points which appear due to vessel breakages, short vessels and spurs. The testing of the proposed methodology is performed using two retinal image databases i.e. VARIA and RIDB. Experimental analysis of given approach shows a significant decrease of false feature points.