{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-one-class-representations-for-face","title":"Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks","arxiv_id":"2007.11457","date":"2020-07-22","proceeding":null,"authors":["Anjith George","Sebastien Marcel"],"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.","url_abs":"https://arxiv.org/abs/2007.11457v1","url_pdf":"https://arxiv.org/pdf/2007.11457v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-one-class-representations-for-face","repo_url":"https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"learning-one-class-representations-for-face","repo_url":"https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"learning-one-class-representations-for-face","repo_url":"https://github.com/anjith2006/bob.paper.oneclass_mccnn_2019","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"face-anti-spoofing","task_name":"Face Anti-Spoofing"},{"task_slug":"face-presentation-attack-detection","task_name":"Face Presentation Attack Detection"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"one-class-classifier","task_name":"One-class classifier"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-anti-spoofing-on-mlfp","task":"Face Anti-Spoofing","dataset":"MLFP","model":"MCCNN (BCE+OCCL)-GMM","rank_in_archive_order":1,"of":1,"metrics":{"HTER":"3.4"},"uses_additional_data":false},{"leaderboard":"/sota/face-presentation-attack-detection-on-wmca","task":"Face Presentation Attack Detection","dataset":"WMCA","model":"MCCNN(BCE+OCCL)-GMM","rank_in_archive_order":1,"of":3,"metrics":{"ACER":"0.097"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2007.11457","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}