%Aigaion2 BibTeX export from Idiap Publications
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         author = {George, Anjith and Marcel, S{\'{e}}bastien},
       projects = {Idiap, ODIN/BATL},
          title = {Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks},
        journal = {IEEE Transactions on Information Forensics and Security},
           year = {2020},
       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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2020/George_TIFS_2020.pdf}