Iris Image Segmentation and Recognition

Abstract

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 live-ness, image segmentation, and image recognition using traditional and statistical methods. Each method has its own strengths and limitations.

In this paper, an iris recognition system is presented with several steps. First, image pre-processing is performed followed by extracting the iris portion from the eye image. The extracted iris part is then normalized, and Iris Code is constructed using daugman rubber sheet. Then the features are extracted by filtering the normalized iris region. This filtering is performed by convolution with a pair of Gabor filters.   Finally two Iris Codes are compared to find the Hamming Distance, which is a fractional measure of the dissimilarity. Experimental image results show that unique codes can be generated for every eye image, extracts the important features from the image, and matches those features with data in an iris database. This approach will be simple and effective. The system is implemented by using Matlab.