{"url":"/dataset/wmca","name":"WMCA","full_name":"Wide Multi Channel Presentation Attack","description_markdown":"The Wide Multi Channel Presentation Attack (WMCA) database consists of 1941 short video recordings of both bonafide and presentation attacks from 72 different identities. The data is recorded from several channels including color, depth, infra-red, and thermal.\r\n\r\nAdditionally, the pulse reading data for bonafide recordings is also provided.\r\n\r\nPreprocessed images for some of the channels are also provided for part of the data used in the reference publication.\r\n\r\nThe WMCA database is produced at Idiap within the framework of “IARPA BATL” and “H2020 TESLA” projects and it is intended for investigation of presentation attack detection (PAD) methods for face recognition systems.","description_withheld":null,"homepage":"https://www.idiap.ch/dataset/wmca","introduced_date":"2019-09-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/biometric-face-presentation-attack-detection-1","title":"Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network","first_author":"Anjith George","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"tasks":[{"name":"Face Presentation Attack Detection","url":"/task/face-presentation-attack-detection","datasets_with_task":"/datasets/task/face-presentation-attack-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["WMCA"],"data_loaders":[],"num_papers_in_archive":33,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/face-presentation-attack-detection-on-wmca","task":"Face Presentation Attack Detection","dataset_variant":"WMCA","rows":3,"metrics":["ACER","ACER@0.2BPCER"],"first_row_in_archive_order":{"model":"MCCNN(BCE+OCCL)-GMM","paper":"/paper/learning-one-class-representations-for-face","metrics":{"ACER":"0.097"},"code_links":[{"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"},{"title":"anjith2006/bob.paper.oneclass_mccnn_2019","url":"https://github.com/anjith2006/bob.paper.oneclass_mccnn_2019"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/learning-one-class-representations-for-face","title":"Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks","date":"2020-07-22","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/can-your-face-detector-do-anti-spoofing-face","title":"Can Your Face Detector Do Anti-spoofing? Face Presentation Attack Detection with a Multi-Channel Face Detector","date":"2020-06-30","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/biometric-face-presentation-attack-detection-1","title":"Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network","date":"2019-09-19","rows_on_this_dataset":1,"code_links":2,"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."}