Texture classification of the human iris using artificial neural networks

Automatic personal identification systems have assumed great importance in the past few years. The iris of the human eye has a texture that is unique for each individual and remains stable over the years. In this paper two feature extraction techniques that are based on 2D Gabor wavelets and 2D DCT are suggested and compared with each other. The features obtained are fed to neural network classifiers for identification. The achieved recognition rate using the DCT coefficients was about 96% compared to 92% obtained using the Gabor coefficients.