  Face recognition is a mainstream biometric authentication method. However,
vulnerability to presentation attacks (a.k.a spoofing) limits its usability in
unsupervised applications. Even though there are many methods available for
tackling presentation attacks (PA), most of them fail to detect sophisticated
attacks such as silicone masks. As the quality of presentation attack
instruments improves over time, achieving reliable PA detection with visual
spectra alone remains very challenging. We argue that analysis in multiple
channels might help to address this issue. In this context, we propose a
multi-channel Convolutional Neural Network based approach for presentation
attack detection (PAD). We also introduce the new Wide Multi-Channel
presentation Attack (WMCA) database for face PAD which contains a wide variety
of 2D and 3D presentation attacks for both impersonation and obfuscation
attacks. Data from different channels such as color, depth, near-infrared and
thermal are available to advance the research in face PAD. The proposed method
was compared with feature-based approaches and found to outperform the
baselines achieving an ACER of 0.3% on the introduced dataset. The database and
the software to reproduce the results are made available publicly.
