Use of Histogram Distances in Iris Authentication

Quantitatively establishing the discriminative power of iris biometric data is considered. Multi-level 2D wavelet transform has been widely used for iris verification system. While previous approaches compute only means and variances, we propose using a histogram distance. We also use a methodology to establish a measure of discrimination that is statistically inferable. To establish the inherent distinctness of the classes, i.e., validate individuality, we transform the many class problem into a dichotomy by using a “distance” between two samples of the same person and between those of two different peoples. We demonstrate that using histogram matching results better performances than using only means and variances.