Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks

• URL: http://arxiv.org/abs/2007.11457v1
• Date: Wed, 22 Jul 2020 14:19:33 GMT
• Title: Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks
• Authors: Anjith George and Sebastien Marcel
• Abstract summary: presentation attack detection (PAD) methods often fail in generalizing to unseen attacks. 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. The proposed framework introduces a novel approach to learn a robust PAD system from bonafide and available (known) attack classes.
• Score: 7.665392786787577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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