{"url":"/dataset/fer","name":"FER+","full_name":"Face Expression Recognition Plus dataset","description_markdown":"The **FER+** dataset is an extension of the original FER dataset, where the images have been re-labelled into one of 8 emotion types: neutral, happiness, surprise, sadness, anger, disgust, fear, and contempt.\r\n\r\nSource: [https://github.com/Microsoft/FERPlus](https://github.com/Microsoft/FERPlus)\r\nImage Source: [https://github.com/Microsoft/FERPlus](https://github.com/Microsoft/FERPlus)","description_withheld":null,"homepage":"https://github.com/Microsoft/FERPlus","introduced_date":"2016-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/training-deep-networks-for-facial-expression","title":"Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution","first_author":"Emad Barsoum","url":null},"license":{"name":"Custom","url":"https://github.com/microsoft/FERPlus/blob/master/LICENSE.md"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Facial Expression Recognition (FER)","url":"/task/facial-expression-recognition","datasets_with_task":"/datasets/task/facial-expression-recognition"},{"name":"Facial Expression Recognition","url":"/task/facial-expression-recognition-1","datasets_with_task":"/datasets/task/facial-expression-recognition-1"}],"languages":[],"variants":["FER+","FERPlus"],"data_loaders":[{"repo":"https://github.com/Microsoft/FERPlus","url":"https://github.com/Microsoft/FERPlus","frameworks":[]}],"num_papers_in_archive":124,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/facial-expression-recognition-on-fer-1","task":"Facial Expression Recognition (FER)","dataset_variant":"FER+","rows":14,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"PAtt-Lite","paper":"/paper/patt-lite-lightweight-patch-and-attention","metrics":{"Accuracy":"95.55"},"code_links":[{"title":"jlrex/patt-lite","url":"https://github.com/jlrex/patt-lite"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/facial-expression-recognition-on-ferplus","task":"Facial Expression Recognition (FER)","dataset_variant":"FERPlus","rows":4,"metrics":["Accuracy(pretrained)"],"first_row_in_archive_order":{"model":"KTN","paper":"/paper/adaptively-learning-facial-expression-1","metrics":{"Accuracy(pretrained)":"90.49"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/facial-expression-recognition-on-fer-2","task":"Facial Expression Recognition","dataset_variant":"FER+","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"ARBEx","paper":"/paper/arbex-attentive-feature-extraction-with","metrics":{"Accuracy":"93.09"},"code_links":[{"title":"takihasan/arbex","url":"https://github.com/takihasan/arbex"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/qcs-feature-refining-from-quadruplet-cross","title":"QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition","date":"2024-11-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/grefel-geometry-aware-reliable-facial","title":"GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution","date":"2024-10-21","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/real-time-emotion-analysis-using-deep","title":"Real Time Emotion Analysis Using Deep Learning for Education, Entertainment, and Beyond","date":"2024-07-05","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-dual-direction-attention-mixed-feature","title":"A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition","date":"2023-08-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/patt-lite-lightweight-patch-and-attention","title":"PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition","date":"2023-06-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/arbex-attentive-feature-extraction-with","title":"ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning","date":"2023-05-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/facial-expression-recognition-using-vanilla","title":"Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers","date":"2022-07-22","rows_on_this_dataset":3,"code_links":0,"syntology":null},{"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":"2026-09-24T18:15:14+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-recognition-with-grid-wise","title":"Facial expression recognition with grid-wise attention and visual transformer","date":"2021-08-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/adaptively-learning-facial-expression-1","title":"Adaptively Learning Facial Expression Representation via C-F Labels and Distillation","date":"2021-01-13","rows_on_this_dataset":2,"code_links":0,"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":1,"code_links":0,"syntology":null},{"paper":"/paper/efficient-facial-feature-learning-with-wide","title":"Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks","date":"2020-01-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/emotion-recognition-in-speech-using-cross","title":"Emotion Recognition in Speech using Cross-Modal Transfer in the Wild","date":"2018-08-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/local-learning-with-deep-and-handcrafted","title":"Local Learning with Deep and Handcrafted Features for Facial Expression Recognition","date":"2018-04-29","rows_on_this_dataset":2,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":4,"samples_ran":1,"samples_unverified":3,"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."}