{"url":"/dataset/oulu-npu","name":"OULU-NPU","full_name":null,"description_markdown":"The Oulu-NPU face presentation attack detection database consists of 4950 real access and attack videos. These videos were recorded using the front cameras of six mobile devices (Samsung Galaxy S6 edge, HTC Desire EYE, MEIZU X5, ASUS Zenfone Selfie, Sony XPERIA C5 Ultra Dual and OPPO N3) in three sessions with different illumination conditions and background scenes. The presentation attack types considered in the OULU-NPU database are print and video-replay. The 2D face artefacts were created using two printers and two display devices. \r\n\r\nThe videos of the 55 subjects are divided into three subject-disjoint subsets for training, development and testing. Four test protocols are used to evaluate the generalization capability of face PAD methods across three covariates: unknown environmental conditions (namely illumination and background scene), acquisition devices and presentation attack instruments (PAI). Each of the four unambiguously defined evaluation protocols introduces at least one previously unseen condition to the test set, which enables a fair comparison on the generalization capabilities between new and existing approaches.\r\n\r\nImage Source: [https://www.researchgate.net/profile/Neil-Robertson/publication/333834759/figure/fig5/AS:897964780302339@1591102895306/Samples-from-the-OULU-NPU-database-From-top-to-bottom-is-the-three-sessions-with_W640.jpg](https://www.researchgate.net/profile/Neil-Robertson/publication/333834759/figure/fig5/AS:897964780302339@1591102895306/Samples-from-the-OULU-NPU-database-From-top-to-bottom-is-the-three-sessions-with_W640.jpg)","description_withheld":null,"homepage":"https://sites.google.com/site/oulunpudatabase/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":{"name":"Custom (research-only, non-commercial)","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"},{"name":"Face Presentation Attack Detection","url":"/task/face-presentation-attack-detection","datasets_with_task":"/datasets/task/face-presentation-attack-detection"}],"languages":[],"variants":["OULU-NPU"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/face-anti-spoofing-on-oulu-npu","task":"Face Anti-Spoofing","dataset_variant":"OULU-NPU","rows":4,"metrics":["ACER","HTER"],"first_row_in_archive_order":{"model":"Bi-FPNFAS","paper":"/paper/bi-fpnfas-bi-directional-feature-pyramid","metrics":{"ACER":"2.92"},"code_links":[{"title":"FaceOnLive/Face-Liveness-Detection-SDK-Linux","url":"https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/generalizable-method-for-face-anti-spoofing","title":"Generalizable Method for Face Anti-Spoofing with Semi-Supervised Learning","date":"2022-06-13","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/bi-fpnfas-bi-directional-feature-pyramid","title":"Bi-FPNFAS: Bi-Directional Feature Pyramid Network for Pixel-Wise Face Anti-Spoofing by Leveraging Fourier Spectra","date":"2021-04-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-deeppixbis-attentional-angular-margin-for","title":"A-DeepPixBis: Attentional Angular Margin for Face Anti-Spoofing","date":"2021-03-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/searching-central-difference-convolutional","title":"Searching Central Difference Convolutional Networks for Face Anti-Spoofing","date":"2020-03-09","rows_on_this_dataset":1,"code_links":5,"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."}