{"url":"/dataset/disfa","name":"DISFA","full_name":"Denver Intensity of Spontaneous Facial Action","description_markdown":"The **Denver Intensity of Spontaneous Facial Action** (**DISFA**) dataset consists of 27 videos of 4844 frames each, with 130,788 images in total. Action unit annotations are on different levels of intensity, which are ignored in the following experiments and action units are either set or unset. DISFA was selected from a wider range of databases popular in the field of facial expression recognition because of the high number of smiles, i.e. action unit 12. In detail, 30,792 have this action unit set, 82,176 images have some action unit(s) set and 48,612 images have no action unit(s) set at all.\r\n\r\nSource: [Deep Learning For Smile Recognition](https://arxiv.org/abs/1602.00172)\r\nImage Source: [https://www.researchgate.net/figure/Examples-of-images-extracted-from-the-DISFA-dataset_fig5_301830237](https://www.researchgate.net/figure/Examples-of-images-extracted-from-the-DISFA-dataset_fig5_301830237)","description_withheld":null,"homepage":"http://mohammadmahoor.com/disfa/","introduced_date":"2013-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"DISFA: A Spontaneous Facial Action Intensity Database","first_author":null,"url":"https://doi.org/10.1109/T-AFFC.2013.4"},"license":{"name":"Custom (research-only, attribution)","url":"http://mohammadmahoor.com/disfa-contact-form/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Facial Expression Recognition (FER)","url":"/task/facial-expression-recognition","datasets_with_task":"/datasets/task/facial-expression-recognition"},{"name":"Facial Action Unit Detection","url":"/task/facial-action-unit-detection","datasets_with_task":"/datasets/task/facial-action-unit-detection"},{"name":"Smile Recognition","url":"/task/smile-recognition","datasets_with_task":"/datasets/task/smile-recognition"}],"languages":[],"variants":["DISFA"],"data_loaders":[{"repo":"https://github.com/forever208/fmae-iat","url":"https://github.com/forever208/fmae-iat","frameworks":["pytorch"]}],"num_papers_in_archive":148,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/facial-action-unit-detection-on-disfa","task":"Facial Action Unit Detection","dataset_variant":"DISFA","rows":8,"metrics":["Average F1","Average AUC"],"first_row_in_archive_order":{"model":"Norface","paper":"/paper/norface-improving-facial-expression-analysis","metrics":{"Average F1":"72.7"},"code_links":[{"title":"liuhw01/Norface","url":"https://github.com/liuhw01/Norface"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/facial-expression-recognition-on-disfa","task":"Facial Expression Recognition (FER)","dataset_variant":"DISFA","rows":2,"metrics":["ICC"],"first_row_in_archive_order":{"model":"Norface","paper":"/paper/norface-improving-facial-expression-analysis","metrics":{"ICC":"0.67"},"code_links":[{"title":"liuhw01/Norface","url":"https://github.com/liuhw01/Norface"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/smile-recognition-on-disfa","task":"Smile Recognition","dataset_variant":"DISFA","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Deep CNN","paper":"/paper/deep-learning-for-smile-recognition","metrics":{"Accuracy":"99.45%"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/norface-improving-facial-expression-analysis","title":"Norface: Improving Facial Expression Analysis by Identity Normalization","date":"2024-07-22","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/representation-learning-and-identity","title":"Representation Learning and Identity Adversarial Training for Facial Behavior Understanding","date":"2024-07-15","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/multi-scale-dynamic-and-hierarchical","title":"Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition","date":"2024-04-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":9,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-multi-dimensional-edge-feature-based","title":"Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition","date":"2022-05-02","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":7,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pre-training-strategies-and-datasets-for","title":"Pre-training strategies and datasets for facial representation learning","date":"2021-03-30","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/deep-adaptive-attention-for-joint-facial","title":"Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment","date":"2018-03-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-region-and-multi-label-learning-for","title":"Deep Region and Multi-Label Learning for Facial Action Unit Detection","date":"2016-06-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/deep-learning-for-smile-recognition","title":"Deep Learning For Smile Recognition","date":"2016-01-30","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":18,"samples_ran":16,"samples_unverified":2,"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."}