Extracting the combination featuresbased on the binary genetic to improve the diagnostic of iris recognition systems

Iris recognition system consists of some stages where feature extraction is one of the most important one. Most of the available systems uses one special technic to extract features. To improve performance of the system, we used a binary genetic algorithm with a novel fitness function to find a combinational feature extraction method. Proposed method uses many different filters and transformations which were applied in feature extraction of iris and finds the best combination during iteration of the algorithm. Consequently a set of methods including numbers of wavelet transform, Gabor filter and Fourier transform is achieved as the best combinational feature extraction approach. In experiments, improving performance of the proposed combinational approach is shown contrasting to other single methods using ROC curve. Comparisons showed that the proposed method outperforms other state of the art methods in most of cases. This method succeeded to achieve FAR=0 and FRR=0.092.