{"url":"/dataset/acted-facial-expressions-in-the-wild-afew","name":"Acted Facial Expressions In The Wild (AFEW)","full_name":null,"description_markdown":"Acted Facial Expressions In The Wild (AFEW) is a dynamic temporal facial expressions data corpus consisting of close to real world environment extracted from movie","description_withheld":null,"homepage":"https://cs.anu.edu.au/few/AFEW.html","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Facial Expression Recognition (FER)","url":"/task/facial-expression-recognition","datasets_with_task":"/datasets/task/facial-expression-recognition"}],"languages":[],"variants":["Acted Facial Expressions In The Wild (AFEW)"],"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/facial-expression-recognition-on-acted-facial","task":"Facial Expression Recognition (FER)","dataset_variant":"Acted Facial Expressions In The Wild (AFEW)","rows":8,"metrics":["Accuracy(on validation set)"],"first_row_in_archive_order":{"model":"ResNet50","paper":"/paper/exploring-emotion-features-and-fusion","metrics":{"Accuracy(on validation set)":"65.5%"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/learn-from-all-erasing-attention-consistency","title":"Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition","date":"2022-07-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/facial-expression-and-attributes-recognition","title":"Facial expression and attributes recognition based on multi-task learning of lightweight neural networks","date":"2021-03-31","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/exploring-emotion-features-and-fusion","title":"Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition","date":"2020-12-27","rows_on_this_dataset":4,"code_links":0,"syntology":null},{"paper":"/paper/noisy-student-training-using-body-language","title":"Noisy Student Training using Body Language Dataset Improves Facial Expression Recognition","date":"2020-08-06","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/frame-attention-networks-for-facial","title":"Frame attention networks for facial expression recognition in videos","date":"2019-06-29","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":1,"samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":1,"papers_with_no_sample_that_ran":1,"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."}