{"url":"/dataset/affectnet","name":"AffectNet","full_name":"burak yılmaz","description_markdown":"**AffectNet** is a large facial expression dataset with around 0.4 million images manually labeled for the presence of eight (neutral, happy, angry, sad, fear, surprise, disgust, contempt) facial expressions along with the intensity of valence and arousal.\r\n\r\nSource: [Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression Recognition](https://arxiv.org/abs/2007.10298)\r\nImage Source: [http://mohammadmahoor.com/affectnet/](http://mohammadmahoor.com/affectnet/)","description_withheld":null,"homepage":"http://mohammadmahoor.com/affectnet/","introduced_date":"2019-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/affectnet-a-database-for-facial-expression","title":"AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild","first_author":"Ali Mollahosseini","url":null},"license":{"name":"Custom (non-commercial)","url":"http://mohammadmahoor.com/affectnet-request-form/"},"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"},{"name":"Arousal Estimation","url":"/task/arousal-estimation","datasets_with_task":"/datasets/task/arousal-estimation"},{"name":"Valence Estimation","url":"/task/valence-estimation","datasets_with_task":"/datasets/task/valence-estimation"},{"name":"Continuous Affect Estimation","url":"/task/continuous-affect-estimation","datasets_with_task":"/datasets/task/continuous-affect-estimation"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Arabic","url":"/datasets/language/arabic"}],"variants":["AffectNet"],"data_loaders":[{"repo":"https://github.com/Huong260303/stm32","url":"https://github.com/Huong260303/stm32","frameworks":["tf"]}],"num_papers_in_archive":323,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/facial-expression-recognition-on-affectnet","task":"Facial Expression Recognition (FER)","dataset_variant":"AffectNet","rows":50,"metrics":["Accuracy (8 emotion)","Accuracy (7 emotion)"],"first_row_in_archive_order":{"model":"Norface","paper":"/paper/norface-improving-facial-expression-analysis","metrics":{"Accuracy (8 emotion)":"68.69"},"code_links":[{"title":"liuhw01/Norface","url":"https://github.com/liuhw01/Norface"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/continuous-affect-estimation-on-affectnet-1","task":"Continuous Affect Estimation","dataset_variant":"AffectNet","rows":1,"metrics":["Concordance correlation coefficient (CCC)"],"first_row_in_archive_order":{"model":"BFsGP","paper":"/paper/facial-geometric-feature-extraction-for","metrics":{"Concordance correlation coefficient (CCC)":"0.7899"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/facial-expression-recognition-on-affectnet-1","task":"Facial Expression Recognition","dataset_variant":"AffectNet","rows":1,"metrics":["Accuracy (7 emotion)"],"first_row_in_archive_order":{"model":"Up-Sampling","paper":"/paper/affectnet-a-database-for-facial-expression","metrics":{"Accuracy (7 emotion)":"-"},"code_links":[{"title":"jonathangiguere/Emotion_Image_Classifier","url":"https://github.com/jonathangiguere/Emotion_Image_Classifier"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/continuous-affect-estimation-on-affectnet","task":"Continuous Affect Estimation","dataset_variant":"AffectNet","rows":0,"metrics":["CCC (Arousal)","CCC (Valence)","RMSE (Valance)"],"first_row_in_archive_order":null,"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/facial-geometric-feature-extraction-for","title":"Facial Geometric Feature Extraction for Dimensional Emotion Analysis Using Genetic Programming","date":"2025-04-17","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-novel-deep-learning-approach-for-facial","title":"A novel deep learning approach for facial emotion recognition: application to detecting emotional responses in elderly individuals with Alzheimer’s disease","date":"2024-12-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/expllm-towards-chain-of-thought-for-facial","title":"ExpLLM: Towards Chain of Thought for Facial Expression Recognition","date":"2024-09-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/resemotenet-bridging-accuracy-and-loss","title":"ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition","date":"2024-09-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-lightweight-model-enhancing-facial","title":"A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine-Harmony Loss","date":"2024-08-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/norface-improving-facial-expression-analysis","title":"Norface: Improving Facial Expression Analysis by Identity Normalization","date":"2024-07-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/representation-learning-and-identity","title":"Representation Learning and Identity Adversarial Training for Facial Behavior Understanding","date":"2024-07-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/batch-transformer-look-for-attention-in-batch","title":"Batch Transformer: Look for Attention in Batch","date":"2024-07-05","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/cage-circumplex-affect-guided-expression","title":"CAGE: Circumplex Affect Guided Expression Inference","date":"2024-04-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/from-static-to-dynamic-adapting-landmark-1","title":"From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos","date":"2023-12-09","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":7,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fernext-facial-expression-recognition-using","title":"FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel Attention","date":"2023-10-20","rows_on_this_dataset":1,"code_links":1,"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":2,"code_links":1,"syntology":null},{"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/poster-v2-a-simpler-and-stronger-facial","title":"POSTER++: A simpler and stronger facial expression recognition network","date":"2023-01-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/in-search-of-a-robust-facial-expressions","title":"In Search of a Robust Facial Expressions Recognition Model: A Large-Scale Visual Cross-Corpus Study","date":"2022-10-07","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; 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not a correctness claim."}},{"paper":"/paper/compacting-picking-and-growing-for","title":"Compacting, Picking and Growing for Unforgetting Continual Learning","date":"2019-10-15","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/expression-affect-action-unit-recognition-aff","title":"Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace","date":"2019-09-25","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/increasingly-packing-multiple-facial","title":"Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning","date":"2019-06-10","rows_on_this_dataset":1,"code_links":2,"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/facial-motion-prior-networks-for-facial","title":"Facial Motion Prior Networks for Facial Expression Recognition","date":"2019-02-23","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/generating-faces-for-affect-analysis","title":"Deep Neural Network Augmentation: Generating Faces for Affect Analysis","date":"2018-11-12","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/cake-compact-and-accurate-k-dimensional","title":"CAKE: Compact and Accurate K-dimensional representation of Emotion","date":"2018-07-30","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":1,"code_links":0,"syntology":null},{"paper":"/paper/affectnet-a-database-for-facial-expression","title":"AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild","date":"2017-08-14","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":22,"samples_ran":10,"samples_unverified":12,"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."}