  Identifying the origin of a sample image in biometric systems can be
beneficial for data authentication in case of attacks against the system and
for initiating sensor-specific processing pipelines in sensor-heterogeneous
environments. Motivated by shortcomings of the photo response non-uniformity
(PRNU) based method in the biometric context, we use a texture classification
approach to detect the origin of finger vein sample images. Based on eight
publicly available finger vein datasets and applying eight classical yet simple
texture descriptors and SVM classification, we demonstrate excellent sensor
model identification results for raw finger vein samples as well as for the
more challenging region of interest data. The observed results establish
texture descriptors as effective competitors to PRNU in finger vein sensor
model identification.
