IRINA: Iris Recognition (Even) in Inaccurately Segmented Data

Abstract

The effectiveness of current iris recognition systems depends on the accurate segmentation and parameterisation of the iris boundaries, as failures at this point misalign the coefficients of the biometric signatures. This paper describes IRINA, an algorithm for Iris Recognition that is robust against INAccurately segmented samples, which makes it a good candidate to work in poor-quality data. The process is based in the concept of "corresponding" patch between pairs of images, that is used to estimate the posterior probabilities that patches regard the same biological region, even in case of segmentation errors and non-linear texture deformations. Such information enables to infer a free-form deformation field (2D registration vectors) between images, whose first and second-order statistics provide effective biometric discriminating power. Extensive experiments were carried out in four datasets (CASIA-IrisV3-Lamp, CASIA-IrisV4-Lamp, CASIA-IrisV4-Thousand and WVU) and show that IRINA not only achieves state-of-the-art performance in good quality data, but also handles effectively severe segmentation errors and large differences in pupillary dilation / constriction.