Analysis of statistical feature extraction for Iris Recognition System using Laplacian of Gaussian filter

Biometrics deals with identification of individuals based on their biological or behavioural characteristics. Iris recognition is one of the newer biometric technologies used for personal identification. It is one of the most reliable and widely used biometric techniques available. In general, a typical iris recognition method includes capturing iris images, testing iris liveness, image segmentation, and image recognition using traditional and statistical methods. Each method has its own strengths and limitations. In this paper, we present a novel approach for statistical feature by using Laplacian of Gaussian filter to iris recognition. Our goal is to develop best algorithm that enhances iris images, reduces noise to the maximum extent possible, extracts the important features from the image, and matches those features with data in an iris database. This approach will be simple and effective, and can be implemented in realtime. Experiments are performed using iris images obtained from CASIA database (Institute of Automation, Chinese Academy of Sciences) and Matlab application for its easy and efficient tools in image manipulation.