PRNU-based finger vein sensor identification: On the effect of different sensor croppings

In this work, we study the applicability of PRNU-based sensor identification methods for finger vein imagery. We also investigate the effect of different image regions on the identification performance by looking at five different crop-pings with different sizes. The proposed method is tested on eight publicly available finger vein datasets. For each finger vein sensor a noise reference pattern is generated and subsequently matched with noise residuals extracted from previously unseen finger vein images. Although the final result strongly encourages the use of PRNU-based approaches for sensor identification, it can also be observed that the choice of image region for PRNU extraction is crucial. The result clearly shows that regions containing biometric trait (varying content) should be preferred over background regions containing non-biometric trait (identical content).