  Despite the high biometric performance, finger-vein recognition systems are
vulnerable to presentation attacks (aka., spoofing attacks). In this paper, we
present a new and robust approach for detecting presentation attacks on
finger-vein biometric systems exploiting the 3D Shape (normal-map) and material
properties (diffuse-map) of the finger. Observing the normal-map and
diffuse-map exhibiting enhanced textural differences in comparison with the
original finger-vein image, especially in the presence of varying illumination
intensity, we propose to employ textural feature-descriptors on both of them
independently. The features are subsequently used to compute a separating
hyper-plane using Support Vector Machine (SVM) classifiers for the features
computed from normal-maps and diffuse-maps independently. Given the scores from
each classifier for normal-map and diffuse-map, we propose sum-rule based score
level fusion to make detection of such presentation attack more robust. To this
end, we construct a new database of finger-vein images acquired using a custom
capture device with three inbuilt illuminations and validate the applicability
of the proposed approach. The newly collected database consists of 936 images,
which corresponds to 468 bona fide images and 468 artefact images. We establish
the superiority of the proposed approach by benchmarking it with classical
textural feature-descriptor applied directly on finger-vein images. The
proposed approach outperforms the classical approaches by providing the Attack
Presentation Classification Error Rate (APCER) & Bona fide Presentation
Classification Error Rate (BPCER) of 0% compared to comparable traditional
methods.
