Iris Recognition Using Wavelet Transform and Artificial Neural Networks

In this approach to get more accuracy of the iris recognition, is composed of many steps: capturing the iris image, determining the location of the iris boundaries, normalization, preprocessed using median filter to remove noise, using wavelet transform for two types of filter, Haar and Daubechies (db4), in order to extract the features and finally using the matching by artificial feed forward neural network with back propagation algorithm (FFBNN) for training and testing iris image. In this proposed system, two database systems are used. The first is CASIA database system (version 1.0) (Chinese Academy of Sciences Institute of Automation). And, the second is REAL database system by using real persons and each person takes many images for recognition through camera Mobile Type of Galaxy Note3. In CASIA System, the iris recognition rate for Haar filter was 84.2% and for Daubechies filter was 92.8%, while in Real system, the iris recognition rate for Haar filter was 90% and for Daubechies filter was 98.7%, this means the Daubechies filter was the best in time and error from the Haar filter. Finally, this system is efficient, because the performance measurement of FAR was 0%. The results and the experiments were implemented by P4 computer and the software package MATLAB (R2011a).