Iris Recognition Using Gabor Filters Optimized by the Particle Swarm Technique

In this paper, an efficient feature extraction algorithm based on optimized Gabor filters and a relative variation analysis approach is presented for iris recognition. The Gabor filters are optimized by tuning the parameters with the particle swarm optimization method. Moreover, a sequential filter scheme is developed to determine the number of filters in the optimal Gabor filter bank. In the preprocessing step, the lower part of the iris image is unwrapped and normalized to a rectangular block which is then decomposed by the optimal Gabor filters. After that, a simple encoding method is adopted to generate a compact iris code. Experimental results show that the performance of the proposed method is encouraging and comparable to those of the existing iris recognition systems.