  This paper proposes a method for utilizing thermal features of the hand for
the purpose of presentation attack detection (PAD) that can be employed in a
hand biometrics system's pipeline. By envisaging two different operational
modes of our system, and by employing a DCNN-based classifiers fine-tuned with
a dataset of real and fake hand representations captured in both visible and
ther- mal spectrum, we were able to bring two important deliverables. First, a
PAD method operating in an open-set mode, capable of correctly discerning 100%
of fake thermal samples, achieving Attack Presentation Classification Error
Rate (APCER) and Bona-Fide Presentation Classification Error Rate (BPCER) equal
to 0%, which can be easily implemented into any existing system as a separate
component. Second, a hand biometrics system operating in a closed-set mode,
that has PAD built right into the recognition pipeline, and operating
simultaneously with the user-wise classification, achieving rank-1 recognition
accuracy of up to 99.75%. We also show that thermal images of the human hand,
in addition to liveness features they carry, can also improve classification
accuracy of a biometric system, when coupled with visible light images. To
follow the reproducibility guidelines and to stimulate further research in this
area, we share the trained model weights, source codes, and a newly created
dataset of fake hand representations with interested researchers.
