Iris recognition for biometric identification using dyadic wavelet transform zero-crossing

A novel biometric identification approach based on the human iris pattern is proposed. The main idea of this technique is to represent the features of the iris by fine-to-coarse approximations at different resolution levels based on the discrete dyadic wavelet transform zero-crossing representation. The resulting one-dimensional (1D) signals are compared with model features using different distances. Before performing the feature extraction, a pre-processing step is to be made by image processing techniques, isolating the iris and enhancing the area of study. The proposed technique is translation, rotation and scale invariant. Results show a classification success above 98%, achieving an equal error rate equal to 0.21% and the possibility of having null false acceptance rates with low false rejection rates.