{"url":"/dataset/sfew","name":"SFEW","full_name":"Static Facial Expression in the Wild","description_markdown":"The Static Facial Expressions in the Wild (**SFEW**) dataset is a dataset for facial expression recognition. It was created by selecting static frames from the AFEW database by computing key frames based on facial point clustering. The most commonly used version, SFEW 2.0, was the benchmarking data for the SReco sub-challenge in EmotiW 2015. SFEW 2.0 has been divided into three sets: Train (958 samples), Val (436 samples) and Test (372 samples). Each of the images is assigned to one of seven expression categories, i.e., anger, disgust, fear, neutral, happiness, sadness, and surprise. The expression labels of the training and validation sets are publicly available, whereas those of the testing set are held back by the challenge organizer.\r\n\r\nSource: [Deep Facial Expression Recognition: A Survey](https://arxiv.org/abs/1804.08348)\r\nImage Source: [https://computervisiononline.com/dataset/1105138659](https://computervisiononline.com/dataset/1105138659)","description_withheld":null,"homepage":"https://cs.anu.edu.au/few/AFEW.html","introduced_date":"2011-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark","first_author":null,"url":"https://doi.org/10.1109/ICCVW.2011.6130508"},"license":{"name":"Unknown","url":null},"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"}],"languages":[],"variants":["SFEW"],"data_loaders":[],"num_papers_in_archive":61,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/facial-expression-recognition-on-sfew","task":"Facial Expression Recognition (FER)","dataset_variant":"SFEW","rows":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Ada-DF","paper":"/paper/a-dual-branch-adaptive-distribution-fusion","metrics":{"Accuracy":"60.46"},"code_links":[{"title":"taylor-xy0827/Ada-DF","url":"https://github.com/taylor-xy0827/Ada-DF"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-dual-branch-adaptive-distribution-fusion","title":"A Dual-Branch Adaptive Distribution Fusion Framework for Real-World Facial Expression Recognition","date":"2023-05-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-vision-transformer-with-squeeze-and","title":"Learning Vision Transformer with Squeeze and Excitation for Facial Expression Recognition","date":"2021-07-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/region-attention-networks-for-pose-and","title":"Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition","date":"2019-05-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/island-loss-for-learning-discriminative","title":"Island Loss for Learning Discriminative Features in Facial Expression Recognition","date":"2017-10-09","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."}