{"url":"/dataset/casia-mfsd","name":"CASIA-MFSD","full_name":"CASIA-MFSD","description_markdown":"**CASIA-MFSD** is a dataset for face anti-spoofing. It contains 50 subjects, and 12 videos for each subject under different resolutions and light conditions. Three different spoof attacks are designed: replay, warp print and cut print attacks. The database contains 600 video recordings, in which 240 videos of 20 subjects are used for training and 360 videos of 30 subjects for testing.\r\n\r\nSource: [Improving Face Anti-Spoofing by 3D Virtual Synthesis](https://arxiv.org/abs/1901.00488)\r\nImage Source: [https://link.springer.com/referenceworkentry/10.1007%2F978-1-4899-7488-4_9067](https://link.springer.com/referenceworkentry/10.1007%2F978-1-4899-7488-4_9067)","description_withheld":null,"homepage":"http://biometrics.idealtest.org/findTotalDbByMode.do?mode=Face","introduced_date":"2012-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"A face antispoofing database with diverse attacks","first_author":null,"url":"https://doi.org/10.1109/ICB.2012.6199754"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Face Anti-Spoofing","url":"/task/face-anti-spoofing","datasets_with_task":"/datasets/task/face-anti-spoofing"}],"languages":[],"variants":["CASIA-MFSD"],"data_loaders":[{"repo":"https://github.com/SoftwareGift/FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019","url":"https://github.com/SoftwareGift/FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019","frameworks":["tf"]}],"num_papers_in_archive":58,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/face-anti-spoofing-on-casia-mfsd","task":"Face Anti-Spoofing","dataset_variant":"CASIA-MFSD","rows":2,"metrics":["EER","HTER"],"first_row_in_archive_order":{"model":"3D Synthesis (balancing sampling)","paper":"/paper/improving-face-anti-spoofing-by-3d-virtual","metrics":{"EER":"2.22","HTER":"1.67"},"code_links":[{"title":"sicxu/Deep3DFaceRecon_pytorch","url":"https://github.com/sicxu/Deep3DFaceRecon_pytorch"},{"title":"FaceOnLive/Face-Liveness-Detection-SDK-Linux","url":"https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux"},{"title":"Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection","url":"https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/improving-face-anti-spoofing-by-3d-virtual","title":"Improving Face Anti-Spoofing by 3D Virtual Synthesis","date":"2019-01-02","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/learn-convolutional-neural-network-for-face","title":"Learn Convolutional Neural Network for Face Anti-Spoofing","date":"2014-08-24","rows_on_this_dataset":1,"code_links":4,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}