Abstract: Automatic human iris verification is an active research area with numerous applications in security purposes. Unfortunately, most of feature extraction methods in human iris verification systems are sensitive to noise, scale and rotation. This paper proposes an integrated hybrid model among Discrete Wavelet Transform, Wavelet Neural Network and Genetic Algorithms for optimizing the feature extraction and verification methods. For any iris image, the wavelet features are extracted by Discrete Wavelet Transform without any dependency on scale and pixels' intensity. Besides, Wavelet Neural Network classifier is integrated as a local optimization method to solve the orientation problem and increase the intrinsic features. In solving the down sample process caused by DWT, each human iris should be characterized by a set of parameters of its optimal wavelet analysis function at a determined analysis level. Thus, distributed Genetic Algorithms, meta-heuristic algorithm, is introduced as a global optimization searching technique to discover the optimal parameter values. The details and limitation of this paper will be discussed where a comparative study should appear. Moreover, conclusions and future work are described.

Elsayed Radwan and Mayada Tarek. “Distributed Optimization Model of Wavelet Neuron for Human Iris Verification”. International Journal of Advanced Computer Science and Applications (IJACSA) 6.12 (2015). http://dx.doi.org/10.14569/IJACSA.2015.061229

@article{Radwan2015,
title = {Distributed Optimization Model of Wavelet Neuron for Human Iris Verification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.061229},
url = {http://dx.doi.org/10.14569/IJACSA.2015.061229},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {12},
author = {Elsayed Radwan and Mayada Tarek}
}