{"url":"/sota/facial-expression-recognition-on-aff-wild2","task":{"name":"Facial Expression Recognition (FER)","url":"/task/facial-expression-recognition","note":null},"dataset":{"name":"Aff-Wild2","url":"/dataset/aff-wild2"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Facial Expression Recognition (FER)** is a computer vision task aimed at identifying and categorizing emotional expressions depicted on a human face. The goal is to automate the process of determining emotions in real-time, by analyzing the various features of a face such as eyebrows, eyes, mouth, and other features, and mapping them to a set of emotions such as anger, fear, surprise, sadness and happiness.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [DeXpression](https://arxiv.org/pdf/1509.05371v2.pdf) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy","UAR"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher","UAR":null}},"counts":{"rows":2,"rows_with_code":1,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"GReFEL","metrics":{"Accuracy":"72.48"},"uses_additional_data":false,"paper_date":"2024-10-21","paper":"/paper/grefel-geometry-aware-reliable-facial","paper_url":"https://arxiv.org/abs/2410.15927v1","paper_title":"GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"EmoAffectNet LSTM","metrics":{"UAR":"52.9"},"uses_additional_data":false,"paper_date":"2022-10-07","paper":"/paper/in-search-of-a-robust-facial-expressions","paper_url":"https://www.sciencedirect.com/science/article/abs/pii/S0925231222012656","paper_title":"In Search of a Robust Facial Expressions Recognition Model: A Large-Scale Visual Cross-Corpus Study","code":"https://github.com/ElenaRyumina/EMO-AffectNetModel","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}