Restoration and Recognition of Distant, Blurry Irises

Abstract

Raw iris images collected outdoors at standoff distances exceeding 25 m are susceptible to noise and atmospheric blur and even under ideal imaging conditions are too degraded to carry out recognition with high accuracy. Traditionally, atmospherically distorted images have been corrected through the use of unique hardware components such as adaptive optics. Here we apply a pure digital image restoration approach to correct for optical aberrations. Image restoration was applied to both single images and image sequences. We propose both a single-frame denoising and deblurring approach, and a multiframe fusion and deblurring approach. To compare performance of the proposed methods, iris recognitions were carried out using the approach of Daugman. Hamming distances (HDs) of computed binary iris codes were measured before and after the restoration. We found the HD decreased from >0.46>0.46 prior to a mean value of <0.39<0.39 for random single images. The multiframe fusion approach produced the most robust restoration and achieved a mean HD for all subjects in our data set of 0.33 while known false matches remained at 0.44. These results show that, when used properly, image restoration approaches do significantly increase recognition performance for known true positives with low increase in false positive detections, and irises can be recognized in turbulent atmospheric conditions.