Iris Identification Using Wavelet Decomposition and Gabor Filter

Biometric authentication has seen a widespread increase in popularity as supporting technology has become common in mass produced consumer electronics. Like fingerprints, each individual has unique patterns in the iris, which makes it a common approach for implementing visual biometric authentication. The paper describes a novel system for extracting the iris pattern and using it for identification of people. The system uses Haar wavelet decomposition and 2D Gabor filtering to extract the pattern data. The pattern data is then used with bitwise XOR comparison for final identification matching. Instead of manually selecting parameters for the Gabor filter, a machine learning method called Particle Swarm Optimization was used. The parameters that gave the best matching result were then implemented in the filter design. The implemented system was evaluated on images obtained from 6 individuals in different settings. The evaluation showed that matching identification could be achieved for the database used. The prepossessing of images with Independent Component Analysis was also used to remove the reflections on the images but that did not improve the classification significantly. Still we were able to perfectly distinguish between the individuals. Further preprocessing and a larger training database would be required to get more general and robust results.