Multiscale analysis for iris biometrics

In this work, a new biometric identification approach based on the human iris pattern is proposed. The main idea of this technique is to represent features of the iris by multiscale analysis of the corresponding discrete dyadic wavelet transform zero-crossing representation. In this study we consider the iris signature as the mean of the gray values of different contours of virtual circles in a determined ring-shaped iris region. The resulting one-dimensional signals are compared with model features using different distances. Results show 98.7% classification success, achieving an equal error rate below 0.2% using Hamming distance, and the possibility of having null false acceptance, rates with low false rejection rates.