  The non-intrusive nature and high accuracy of face recognition algorithms
have led to their successful deployment across multiple applications ranging
from border access to mobile unlocking and digital payments. However, their
vulnerability against sophisticated and cost-effective presentation attack
mediums raises essential questions regarding its reliability. In the
literature, several presentation attack detection algorithms are presented;
however, they are still far behind from reality. The major problem with
existing work is the generalizability against multiple attacks both in the seen
and unseen setting. The algorithms which are useful for one kind of attack
(such as print) perform unsatisfactorily for another type of attack (such as
silicone masks). In this research, we have proposed a deep learning-based
network termed as \textit{MixNet} to detect presentation attacks in
cross-database and unseen attack settings. The proposed algorithm utilizes
state-of-the-art convolutional neural network architectures and learns the
feature mapping for each attack category. Experiments are performed using
multiple challenging face presentation attack databases such as SMAD and Spoof
In the Wild (SiW-M) databases. Extensive experiments and comparison with
existing state of the art algorithms show the effectiveness of the proposed
algorithm.
