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• W3046357449 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." @default.
• W3046357449 created "2020-08-07" @default.
• W3046357449 creator A5002218750 @default.
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• W3046357449 date "2021-01-01" @default.
• W3046357449 modified "2023-10-10" @default.
• W3046357449 title "Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks" @default.
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• W3046357449 doi "https://doi.org/10.1109/tifs.2020.3013214" @default.
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