Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks
Anjith George,Sébastien Marcel +1 more
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TLDR
A new framework for PAD is proposed using a one-class classifier, where the representation used is learned with a Multi-Channel Convolutional Neural Network (MCCNN) and a novel loss function is introduced, which forces the network to learn a compact embedding for bonafide class while being far from the representation of attacks.Abstract:
Face recognition has evolved as a widely used biometric modality. However, its vulnerability against presentation attacks poses a significant security threat. Though presentation attack detection (PAD) methods try to address this issue, they often fail in generalizing to unseen attacks. In this work, we propose a new framework for PAD using a one-class classifier, where the representation used is learned with a Multi-Channel Convolutional Neural Network ( MCCNN ). A novel loss function is introduced, which forces the network to learn a compact embedding for bonafide class while being far from the representation of attacks. A one-class Gaussian Mixture Model is used on top of these embeddings for the PAD task. The proposed framework introduces a novel approach to learn a robust PAD system from bonafide and available (known) attack classes. This is particularly important as collecting bonafide data and simpler attacks are much easier than collecting a wide variety of expensive attacks. The proposed system is evaluated on the publicly available WMCA multi-channel face PAD database, which contains a wide variety of 2D and 3D attacks. Further, we have performed experiments with MLFP and SiW-M datasets using RGB channels only. Superior performance in unseen attack protocols shows the effectiveness of the proposed approach. Software, data, and protocols to reproduce the results are made available publicly.read more
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Proceedings ArticleDOI
Cross Modal Focal Loss for RGBD Face Anti-Spoofing
Anjith George,Sébastien Marcel +1 more
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.
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On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing
Anjith George,Sébastien Marcel +1 more
TL;DR: The proposed approach outperforms the state-of-the-art methods in the zero-shot protocols in the HQ-WMCA and SiW-M datasets by a large margin and achieves a significant boost in cross-database performance as well.
Proceedings ArticleDOI
On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing
Anjith George,Sébastien Marcel +1 more
TL;DR: In this paper, the authors used transfer learning from the vision transformer model for zero-shot anti-spoofing task and achieved state-of-the-art performance in the HQ-WMCA and SiW-M datasets.
Journal ArticleDOI
Learning Multi-Granularity Temporal Characteristics for Face Anti-Spoofing
TL;DR: Wang et al. as discussed by the authors proposed a temporal transformer network (TTN) to learn multi-granularity temporal characteristics for face anti-spoofing, which mainly consists of temporal difference attentions (TDA), a pyramid temporal aggregation (PTA), and a temporal depth difference loss (TDL).
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Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection.
Ajian Liu,Chenxu Zhao,Zitong Yu,Jun Wan,Anyang Su,Xing Liu,Zichang Tan,Sergio Escalera,Junliang Xing,Yanyan Liang,Guodong Guo,Zhen Lei,Stan Z. Li,Du Zhang +13 more
TL;DR: Li et al. as mentioned in this paper proposed a Contrastive Context-Aware Learning (CCL) framework for face presentation attack detection, which leverages rich contexts accurately (e.g., subjects, mask material and lighting) among pairs of live faces and high-fidelity mask attacks.
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