A Preliminary Study on Identifying Sensors from Iris Images

Abstract

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.