Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network

• Published in: IEEE transactions on information forensics and security Vol. 15; no. 1; pp. 42 - 55
• Main Authors: George, Anjith, Mostaani, Zohreh, Geissenbuhler, David, Nikisins, Olegs, Anjos, Andre, Marcel, Sebastien
• Format: Journal Article
• Language: English
• Published: New York IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: anti-spoofing; Artificial neural networks; Authentication; Biometrics; Channels; convolutional neural network; Face; Face recognition; Feature extraction; Image color analysis; Masks; multi-channel sensors; Neural networks; Presentation attack detection; Spoofing; Three-dimensional displays; Two dimensional displays
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2019.2916652

Face recognition is a mainstream biometric authentication method. However, the 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.