Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network
Anjith George,Zohreh Mostaani,David Geissenbuhler,Olegs Nikisins,André Anjos,Sébastien Marcel +5 more
TL;DR: In this article, a multi-channel Convolutional Neural Network-based approach for presentation attack detection (PAD) has been proposed, and the new Wide Multi-Channel presentation Attack (WMCA) database is introduced.
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Abstract: 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.
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Citations
CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing
Shifeng Zhang,Ajian Liu,Jun Wan,Yanyan Liang,Guodong Guo,Sergio Escalera,Hugo Jair Escalante,Stan Z. Li +7 more
- 12 Feb 2020
TL;DR: A novel multi-modal multi-scale fusion method is presented as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales.
209
Face Anti-Spoofing via Adversarial Cross-Modality Translation
TL;DR: In this article, a cross-modal auxiliary network (CMA) is proposed for face anti-spoofing detection, which consists of a modality translation network (MT-Net) and a Modality Assistance Network (MA-Net), which can close the visible gap between different modalities via a generative model that maps inputs from one modality (i.e., RGB) to another ( i.e., NIR).
123
Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks
Guillaume Heusch,Anjith George,David Geissbühler,Zohreh Mostaani,Sébastien Marcel +4 more
- 22 Jul 2020
TL;DR: The best proposed approach is able to almost perfectly detect all impersonation attacks while ensuring low bonafide classification errors, and obtained results show that obfuscation attacks are more difficult to detect.
123
Cross Modal Focal Loss for RGBD Face Anti-Spoofing
Anjith George,Sébastien Marcel +1 more
- 01 Mar 2021
TL;DR: In this paper, a cross-modal focal loss function is proposed to modulate the loss contribution of each channel as a function of the confidence of individual channels, which reduces the impact of overfitting.
A survey on 3D mask presentation attack detection and countermeasures
TL;DR: This work presents a comprehensive overview of the state-of-the-art approaches in 3D mask spoofing and anti-spoofing, including existing databases and countermeasures, and quantitatively compares the performance of differentMask spoofing detection methods on a common ground.
98
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