PRNU-based Detection of Finger Vein Presentation Attacks

Abstract

In this work, we evaluated the effectiveness of the Photo Response Non-Uniformity (PRNU) to detect presentation/ spoofing attacks for finger vein imagery. The performance is evaluated on two publicly-available finger vein presentation/ spoofing attack datasets (IDIAP and SCUT-FVD). Maximum likelihood estimation (MLE) is used to estimate the sensor’s PRNU. To decide whether a query image is real or spoofed, we compare its residual to the estimated sensor PRNU using PCE and NCC as similarity measures. We observe that the classification performance is heavily dependent on the set of images used for PRNU estimation. We assume different degrees of variability in image content caused by distinct light scattering properties in real tissue and artifacts to be one of the main reasons for the differences in classification performance.