  The vulnerability against presentation attacks is a crucial problem
undermining the wide-deployment of face recognition systems. Though
presentation attack detection (PAD) systems try to address this problem, the
lack of generalization and robustness continues to be a major concern. Several
works have shown that using multi-channel PAD systems could alleviate this
vulnerability and result in more robust systems. However, there is a wide
selection of channels available for a PAD system such as RGB, Near Infrared,
Shortwave Infrared, Depth, and Thermal sensors. Having a lot of sensors
increases the cost of the system, and therefore an understanding of the
performance of different sensors against a wide variety of attacks is necessary
while selecting the modalities. In this work, we perform a comprehensive study
to understand the effectiveness of various imaging modalities for PAD. The
studies are performed on a multi-channel PAD dataset, collected with 14
different sensing modalities considering a wide range of 2D, 3D, and partial
attacks. We used the multi-channel convolutional network-based architecture,
which uses pixel-wise binary supervision. The model has been evaluated with
different combinations of channels, and different image qualities on a variety
of challenging known and unknown attack protocols. The results reveal
interesting trends and can act as pointers for sensor selection for
safety-critical presentation attack detection systems. The source codes and
protocols to reproduce the results are made available publicly making it
possible to extend this work to other architectures.
