{"url":"/dataset/casme-ii","name":"CASME II","full_name":"Chinese Academy of Sciences Micro-Expression II","description_markdown":"The Chinese Academy of Sciences Micro-Expression dataset (CASME II) consists of 255 videos, elicited from 26 participants. The videos are recorded using Point Gray GRAS-03K2C camera which has a frame rate of 200fps. The average video length is 0.34s, equivalent to 68 frames. Each video’s emotion label is annotated by two coders, where the reliability is 0.846. \r\n\r\nAll the images are cropped to 170×140 pixels. The ground-truth information provided by the database include the emotion state, the action unit, the onset, apex and offset frame indices. The videos are grouped into seven categories: others (99 videos), disgust (63 videos), happiness (32 videos), repression (27 videos), surprise (25 videos), sadness (7 videos) and fear (2 videos).\r\n\r\nSource: [OFF-ApexNet on Micro-expression Recognition System](https://arxiv.org/pdf/1805.08699.pdf)","description_withheld":null,"homepage":"http://fu.psych.ac.cn/CASME/casme2-en.php","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Micro-Expression Recognition","url":"/task/micro-expression-recognition","datasets_with_task":"/datasets/task/micro-expression-recognition"},{"name":"Micro-expression Generation","url":"/task/micro-expression-generation","datasets_with_task":"/datasets/task/micro-expression-generation"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["CASME II"],"data_loaders":[{"repo":"https://github.com/JayShaun/MICRO-Expression-Recognition-with-deep-learning","url":"http://fu.psych.ac.cn/CASME/casme2-en.php","frameworks":[]},{"repo":"https://github.com/JayShaun/MICRO-Expression-Recognition-with-deep-learning","url":"https://github.com/JayShaun/MICRO-Expression-Recognition-with-deep-learning","frameworks":[]}],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/micro-expression-recognition-on-casme-ii-1","task":"Micro-Expression Recognition","dataset_variant":"CASME II","rows":1,"metrics":["UF1","UAR"],"first_row_in_archive_order":{"model":"HTNet","paper":"/paper/htnet-for-micro-expression-recognition","metrics":{"UAR":"95.16","UF1":"95.32"},"code_links":[{"title":"wangzhifengharrison/htnet","url":"https://github.com/wangzhifengharrison/htnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/htnet-for-micro-expression-recognition","title":"HTNet for micro-expression recognition","date":"2023-07-27","rows_on_this_dataset":1,"code_links":1,"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."}