{"url":"/dataset/imigue","name":"iMiGUE","full_name":null,"description_markdown":"**iMiGUE** is a dataset for emotional artificial intelligence research: identity-free video dataset for Micro-Gesture Understanding and Emotion analysis (iMiGUE). Different from existing public datasets, iMiGUE focuses on nonverbal body gestures without using any identity information, while the predominant researches of emotion analysis concern sensitive biometric data, like face and speech. Most importantly, iMiGUE focuses on micro-gestures, i.e., unintentional behaviors driven by inner feelings, which are different from ordinary scope of gestures from other gesture datasets which are mostly intentionally performed for illustrative purposes. Furthermore, iMiGUE is designed to evaluate the ability of models to analyze the emotional states by integrating information of recognized micro-gesture, rather than just recognizing prototypes in the sequences separately (or isolatedly).\r\n\r\nThe authors collected 359 videos of post match press conferences of Grand Slam tournaments. This dataset contains 72 players from 28 countries and regions covering very continent which enables MGs analysis from diverse cultures. iMiGUE comprises 36 female and 36 male players whose ages are between 17 and 38.","description_withheld":null,"homepage":"https://github.com/linuxsino/iMiGUE","introduced_date":"2021-07-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/imigue-an-identity-free-video-dataset-for-1","title":"iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis","first_author":"Xin Liu","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Micro-gesture Recognition","url":"/task/micro-gesture-recognition","datasets_with_task":"/datasets/task/micro-gesture-recognition"}],"languages":[],"variants":["iMiGUE"],"data_loaders":[],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/micro-gesture-recognition-on-imigue","task":"Micro-gesture Recognition","dataset_variant":"iMiGUE","rows":1,"metrics":["Top 1 Accuracy","Top 5 Accuracy"],"first_row_in_archive_order":{"model":"","paper":"/paper/joint-skeletal-and-semantic-embedding-loss","metrics":{"Top 1 Accuracy":"64.12","Top 5 Accuracy":"91.1"},"code_links":[{"title":"VUT-HFUT/MiGA2023_Track1","url":"https://github.com/VUT-HFUT/MiGA2023_Track1"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/joint-skeletal-and-semantic-embedding-loss","title":"Joint Skeletal and Semantic Embedding Loss for Micro-gesture Classification","date":"2023-07-20","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."}