A Preliminary Study on Identifying Sensors From Iris Images

Nathan Kalka, Nick Bartlow, Bojan Cukic, Arun Ross; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2015, pp. 50-56

In this paper we explore the possibility of examining an iris image and identifying the sensor that was used to acquire it. This is accomplished based on a classical pixel non-uniformity (PNU) noise analysis of the iris sensor. For each iris sensor, a noise reference pattern is generated and subsequently correlated with noise residuals extracted from iris images. We conduct experiments using data from seven iris databases, viz., West Virginia University (WVU) non-ideal, WVU off-angle, Iris Challenge Evaluation (ICE) 1.0, CASIAv2 Device1, CASIAv2-Device2, CASIAv3 interval, and CASIAv3 lamp. Results indicate that iris sensor identification using PNU noise is very encouraging, with rank-1 identification rates ranging from 86%-99% for unit level testing (distinguishing sensors from the same vendor) and 81%-96% for the combination of brand (distinguishing sensors from different vendors) and unit level testing. Our analysis also suggests that in many cases, sensor identification can be performed even with a limited number of training images. We also observe that JPEG compression degrades identification performance, specifically at the sensor unit level.