Identifying Sensors from Fingerprint Images

Abstract

In this paper we study the application of hardware fingerprinting based on PRNU noise analysis of biometric fingerprint devices for sensor identification. For each fingerprint sensor, a noise reference pattern is generated and subsequently correlated with noise residuals extracted from test images. We experiment on three different databases including a total of 20 fingerprint sensors. Our results indicate that fingerprint sensor identification at unit level is attainable with promising prospects. Our analysis indicates that in many cases identification can be performed even when one only has access to a limited number of samples. For two of the three databases one can train on less than 8 images per device and establish sensor identification with little or no misclassification error. On the third database, high levels of identification performance can be achieved when training on similar amounts of images required for other types of sensor identification such as cameras or scanners.