  Face recognition has evolved as a prominent biometric authentication
modality. However, vulnerability to presentation attacks curtails its reliable
deployment. Automatic detection of presentation attacks is essential for secure
use of face recognition technology in unattended scenarios. In this work, we
introduce a Convolutional Neural Network (CNN) based framework for presentation
attack detection, with deep pixel-wise supervision. The framework uses only
frame level information making it suitable for deployment in smart devices with
minimal computational and time overhead. We demonstrate the effectiveness of
the proposed approach in public datasets for both intra as well as
cross-dataset experiments. The proposed approach achieves an HTER of 0% in
Replay Mobile dataset and an ACER of 0.42% in Protocol-1 of OULU dataset
outperforming state of the art methods.
