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

TL;DR

This paper presents a multi-channel CNN approach for face presentation attack detection, leveraging diverse spectral data to improve robustness against sophisticated spoofing methods, and introduces a comprehensive new database for benchmarking.

Contribution

The paper introduces a novel multi-channel CNN method and a new wide dataset for face PAD, enhancing detection of complex presentation attacks.

Findings

Achieved an ACER of 0.3% on the new dataset.

Outperformed feature-based approaches in PAD accuracy.

Provided publicly available dataset and software for research.

Abstract

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.