{"url":"/sota/facial-expression-recognition-on-affectnet","task":{"name":"Facial Expression Recognition (FER)","url":"/task/facial-expression-recognition","note":null},"dataset":{"name":"AffectNet","url":"/dataset/affectnet"},"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 (8 emotion)","Accuracy (7 emotion)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy (8 emotion)":"higher","Accuracy (7 emotion)":"higher"}},"counts":{"rows":50,"rows_with_code":31,"rows_with_paper_page":50,"rows_dated":50,"rows_using_additional_data":13},"rows":[{"rank_in_archive_order":1,"model":"Norface","metrics":{"Accuracy (8 emotion)":"68.69"},"uses_additional_data":false,"paper_date":"2024-07-22","paper":"/paper/norface-improving-facial-expression-analysis","paper_url":"https://arxiv.org/abs/2407.15617v1","paper_title":"Norface: Improving Facial Expression Analysis by Identity Normalization","code":"https://github.com/liuhw01/Norface","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"DDAMFN++","metrics":{"Accuracy (7 emotion)":"67.36","Accuracy (8 emotion)":"65.04"},"uses_additional_data":false,"paper_date":"2023-08-25","paper":"/paper/a-dual-direction-attention-mixed-feature","paper_url":"https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=P4efBMcAAAAJ&citation_for_view=P4efBMcAAAAJ:d1gkVwhDpl0C","paper_title":"A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition","code":"https://github.com/simon20010923/DDAMFN","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"FMAE","metrics":{"Accuracy (8 emotion)":"64.79"},"uses_additional_data":true,"paper_date":"2024-07-15","paper":"/paper/representation-learning-and-identity","paper_url":"https://arxiv.org/abs/2407.11243v2","paper_title":"Representation Learning and Identity Adversarial Training for Facial Behavior Understanding","code":"https://github.com/forever208/fmae-iat","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"QCS","metrics":{"Accuracy (7 emotion)":"67.94","Accuracy (8 emotion)":"64.4"},"uses_additional_data":false,"paper_date":"2024-11-04","paper":"/paper/qcs-feature-refining-from-quadruplet-cross","paper_url":"https://arxiv.org/abs/2411.01988v5","paper_title":"QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition","code":"https://github.com/birdwcp/qcs","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"BTN","metrics":{"Accuracy (7 emotion)":"67.60","Accuracy (8 emotion)":"64.29"},"uses_additional_data":false,"paper_date":"2024-07-05","paper":"/paper/batch-transformer-look-for-attention-in-batch","paper_url":"https://arxiv.org/abs/2407.04218v1","paper_title":"Batch Transformer: Look for Attention in Batch","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":6,"model":"DDAMFN","metrics":{"Accuracy (7 emotion)":"67.03","Accuracy (8 emotion)":"64.25"},"uses_additional_data":false,"paper_date":"2023-08-25","paper":"/paper/a-dual-direction-attention-mixed-feature","paper_url":"https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=P4efBMcAAAAJ&citation_for_view=P4efBMcAAAAJ:d1gkVwhDpl0C","paper_title":"A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition","code":"https://github.com/simon20010923/DDAMFN","n_code_links":1,"syntology":null},{"rank_in_archive_order":7,"model":"EmoNeXt","metrics":{"Accuracy (7 emotion)":"67.46","Accuracy (8 emotion)":"64.13"},"uses_additional_data":false,"paper_date":"2024-12-30","paper":"/paper/a-novel-deep-learning-approach-for-facial","paper_url":"https://link.springer.com/article/10.1007/s00521-024-10938-0","paper_title":"A novel deep learning approach for facial emotion recognition: application to detecting emotional responses in elderly individuals with Alzheimer’s disease","code":"https://github.com/yelboudouri/EmoNeXt","n_code_links":1,"syntology":null},{"rank_in_archive_order":8,"model":"POSTER++","metrics":{"Accuracy (7 emotion)":"67.49","Accuracy (8 emotion)":"63.77"},"uses_additional_data":false,"paper_date":"2023-01-28","paper":"/paper/poster-v2-a-simpler-and-stronger-facial","paper_url":"https://arxiv.org/abs/2301.12149v2","paper_title":"POSTER++: A simpler and stronger facial expression recognition network","code":"https://github.com/talented-q/poster_v2","n_code_links":1,"syntology":null},{"rank_in_archive_order":9,"model":"LFNSB","metrics":{"Accuracy (7 emotion)":"66.57","Accuracy (8 emotion)":"63.12"},"uses_additional_data":false,"paper_date":"2024-08-01","paper":"/paper/a-lightweight-model-enhancing-facial","paper_url":"https://www.preprints.org/manuscript/202408.1304/v1","paper_title":"A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine-Harmony Loss","code":"https://github.com/1chenchen22/LFNSB","n_code_links":1,"syntology":null},{"rank_in_archive_order":10,"model":"S2D","metrics":{"Accuracy (7 emotion)":"67.62","Accuracy (8 emotion)":"63.06"},"uses_additional_data":false,"paper_date":"2023-12-09","paper":"/paper/from-static-to-dynamic-adapting-landmark-1","paper_url":"https://arxiv.org/abs/2312.05447v2","paper_title":"From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos","code":"https://github.com/msa-lmc/s2d","n_code_links":2,"syntology":{"n_ran":7,"n_unverified":7,"n_samples":14,"n_pointer_only_licence":0}},{"rank_in_archive_order":11,"model":"Multi-task EfficientNet-B2","metrics":{"Accuracy (7 emotion)":"66.29","Accuracy (8 emotion)":"63.03"},"uses_additional_data":false,"paper_date":"2022-07-04","paper":"/paper/classifying-emotions-and-engagement-in-online","paper_url":"https://ieeexplore.ieee.org/document/9815154","paper_title":"Classifying emotions and engagement in online learning based on a single facial expression recognition neural network","code":"https://github.com/HSE-asavchenko/face-emotion-recognition","n_code_links":2,"syntology":null},{"rank_in_archive_order":12,"model":"MT-ArcRes","metrics":{"Accuracy (8 emotion)":"63"},"uses_additional_data":false,"paper_date":"2019-09-25","paper":"/paper/expression-affect-action-unit-recognition-aff","paper_url":"https://arxiv.org/abs/1910.04855v1","paper_title":"Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":13,"model":"ExpLLM","metrics":{"Accuracy (7 emotion)":"65.93","Accuracy (8 emotion)":"62.86"},"uses_additional_data":false,"paper_date":"2024-09-04","paper":"/paper/expllm-towards-chain-of-thought-for-facial","paper_url":"https://arxiv.org/abs/2409.02828v1","paper_title":"ExpLLM: Towards Chain of Thought for Facial Expression Recognition","code":"https://github.com/starhiking/ExpLLM-TMM","n_code_links":1,"syntology":null},{"rank_in_archive_order":14,"model":"Vit-base + MAE","metrics":{"Accuracy (8 emotion)":"62.42"},"uses_additional_data":false,"paper_date":"2022-07-22","paper":"/paper/facial-expression-recognition-using-vanilla","paper_url":"https://arxiv.org/abs/2207.11081v4","paper_title":"Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":15,"model":"CAGE","metrics":{"Accuracy (7 emotion)":"66.6","Accuracy (8 emotion)":"62.2"},"uses_additional_data":false,"paper_date":"2024-04-23","paper":"/paper/cage-circumplex-affect-guided-expression","paper_url":"https://arxiv.org/abs/2404.14975v1","paper_title":"CAGE: Circumplex Affect Guided Expression Inference","code":"https://github.com/wagner-niklas/cage_expression_inference","n_code_links":1,"syntology":null},{"rank_in_archive_order":16,"model":"DAN","metrics":{"Accuracy (7 emotion)":"65.69","Accuracy (8 emotion)":"62.09"},"uses_additional_data":false,"paper_date":"2021-09-15","paper":"/paper/distract-your-attention-multi-head-cross","paper_url":"https://arxiv.org/abs/2109.07270v6","paper_title":"Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition","code":"https://github.com/yaoing/dan","n_code_links":2,"syntology":null},{"rank_in_archive_order":17,"model":"SL + SSL in-panting-pl (B0)","metrics":{"Accuracy (8 emotion)":"61.72"},"uses_additional_data":false,"paper_date":"2021-05-13","paper":"/paper/using-self-supervised-co-training-to-improve","paper_url":"https://arxiv.org/abs/2105.06421v3","paper_title":"Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":18,"model":"Distilled student","metrics":{"Accuracy (7 emotion)":"65.4","Accuracy (8 emotion)":"61.60"},"uses_additional_data":true,"paper_date":"2021-03-16","paper":"/paper/leveraging-recent-advances-in-deep-learning","paper_url":"https://arxiv.org/abs/2103.09154v2","paper_title":"Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":19,"model":"Multi-task EfficientNet-B0","metrics":{"Accuracy (7 emotion)":"65.74","Accuracy (8 emotion)":"61.32"},"uses_additional_data":true,"paper_date":"2021-03-31","paper":"/paper/facial-expression-and-attributes-recognition","paper_url":"https://arxiv.org/abs/2103.17107","paper_title":"Facial expression and attributes recognition based on multi-task learning of lightweight neural networks","code":"https://github.com/HSE-asavchenko/face-emotion-recognition","n_code_links":2,"syntology":null},{"rank_in_archive_order":20,"model":"SL + SSL puzzling (B2)","metrics":{"Accuracy (8 emotion)":"61.32"},"uses_additional_data":false,"paper_date":"2021-05-13","paper":"/paper/using-self-supervised-co-training-to-improve","paper_url":"https://arxiv.org/abs/2105.06421v3","paper_title":"Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":21,"model":"SL + SSL puzzling (B0)","metrics":{"Accuracy (8 emotion)":"61.09"},"uses_additional_data":false,"paper_date":"2021-05-13","paper":"/paper/using-self-supervised-co-training-to-improve","paper_url":"https://arxiv.org/abs/2105.06421v3","paper_title":"Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":22,"model":"PSR (VGG-16)","metrics":{"Accuracy (7 emotion)":"-","Accuracy (8 emotion)":"60.68"},"uses_additional_data":true,"paper_date":"2020-07-17","paper":"/paper/pyramid-with-super-resolution-for-in-the-wild","paper_url":"https://doi.org/10.1109/ACCESS.2020.3010018","paper_title":"Pyramid With Super Resolution for In-the-Wild Facial Expression Recognition","code":"https://github.com/thanhhungqb/pyramid-super-resolution","n_code_links":1,"syntology":null},{"rank_in_archive_order":23,"model":"VGG-FACE","metrics":{"Accuracy (8 emotion)":"60.40"},"uses_additional_data":true,"paper_date":"2018-11-12","paper":"/paper/generating-faces-for-affect-analysis","paper_url":"https://arxiv.org/abs/1811.05027v2","paper_title":"Deep Neural Network Augmentation: Generating Faces for Affect Analysis","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":24,"model":"SL (B2)","metrics":{"Accuracy (8 emotion)":"60.35"},"uses_additional_data":true,"paper_date":"2021-05-13","paper":"/paper/using-self-supervised-co-training-to-improve","paper_url":"https://arxiv.org/abs/2105.06421v3","paper_title":"Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":25,"model":"SL (B0)","metrics":{"Accuracy (8 emotion)":"60.34"},"uses_additional_data":false,"paper_date":"2021-05-13","paper":"/paper/using-self-supervised-co-training-to-improve","paper_url":"https://arxiv.org/abs/2105.06421v3","paper_title":"Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":26,"model":"MA-Net","metrics":{"Accuracy (7 emotion)":"64.53","Accuracy (8 emotion)":"60.29"},"uses_additional_data":false,"paper_date":"2021-07-05","paper":"/paper/learning-deep-global-multi-scale-and-local","paper_url":"https://ieeexplore.ieee.org/document/9474949","paper_title":"Learning Deep Global Multi-scale and Local Attention Features for Facial Expression Recognition in the Wild","code":"https://github.com/zengqunzhao/ma-net","n_code_links":1,"syntology":null},{"rank_in_archive_order":27,"model":"EfficientFace","metrics":{"Accuracy (7 emotion)":"63.70","Accuracy (8 emotion)":"59.89"},"uses_additional_data":false,"paper_date":"2021-05-18","paper":"/paper/robust-lightweight-facial-expression","paper_url":"https://ojs.aaai.org/index.php/AAAI/article/view/16465","paper_title":"Robust Lightweight Facial Expression Recognition Network with Label Distribution Training","code":"https://github.com/zengqunzhao/efficientface","n_code_links":1,"syntology":null},{"rank_in_archive_order":28,"model":"CNNs and BOVW + local SVM","metrics":{"Accuracy (7 emotion)":"63.31","Accuracy (8 emotion)":"59.58"},"uses_additional_data":false,"paper_date":"2018-04-29","paper":"/paper/local-learning-with-deep-and-handcrafted","paper_url":"https://arxiv.org/abs/1804.10892v7","paper_title":"Local Learning with Deep and Handcrafted Features for Facial Expression Recognition","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":29,"model":"RAN (ResNet-18+)","metrics":{"Accuracy (7 emotion)":"-","Accuracy (8 emotion)":"59.5"},"uses_additional_data":true,"paper_date":"2019-05-10","paper":"/paper/region-attention-networks-for-pose-and","paper_url":"https://arxiv.org/abs/1905.04075v2","paper_title":"Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition","code":"https://github.com/kaiwang960112/Challenge-condition-FER-dataset","n_code_links":1,"syntology":null},{"rank_in_archive_order":30,"model":"Ensemble with Shared Representations (ESR-9)","metrics":{"Accuracy (7 emotion)":"-","Accuracy (8 emotion)":"59.3"},"uses_additional_data":false,"paper_date":"2020-01-17","paper":"/paper/efficient-facial-feature-learning-with-wide","paper_url":"https://arxiv.org/abs/2001.06338v1","paper_title":"Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks","code":"https://github.com/siqueira-hc/Efficient-Facial-Feature-Learning-with-Wide-Ensemble-based-Convolutional-Neural-Networks","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":2,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":31,"model":"ViT-tiny","metrics":{"Accuracy (8 emotion)":"58.28"},"uses_additional_data":false,"paper_date":"2022-07-22","paper":"/paper/facial-expression-recognition-using-vanilla","paper_url":"https://arxiv.org/abs/2207.11081v4","paper_title":"Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":32,"model":"Weighted-Loss","metrics":{"Accuracy (7 emotion)":"-","Accuracy (8 emotion)":"58.0"},"uses_additional_data":false,"paper_date":"2017-08-14","paper":"/paper/affectnet-a-database-for-facial-expression","paper_url":"http://arxiv.org/abs/1708.03985v4","paper_title":"AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild","code":"https://github.com/jonathangiguere/Emotion_Image_Classifier","n_code_links":1,"syntology":null},{"rank_in_archive_order":33,"model":"ViT-base","metrics":{"Accuracy (8 emotion)":"57.99"},"uses_additional_data":false,"paper_date":"2022-07-22","paper":"/paper/facial-expression-recognition-using-vanilla","paper_url":"https://arxiv.org/abs/2207.11081v4","paper_title":"Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":34,"model":"SL+ SSL in-painting-pl + 20% train (B0)","metrics":{"Accuracy (8 emotion)":"55.36"},"uses_additional_data":false,"paper_date":"2021-05-13","paper":"/paper/using-self-supervised-co-training-to-improve","paper_url":"https://arxiv.org/abs/2105.06421v3","paper_title":"Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":35,"model":"SL+ SSL puzzling + 20% train (B0)","metrics":{"Accuracy (8 emotion)":"54.98"},"uses_additional_data":false,"paper_date":"2021-05-13","paper":"/paper/using-self-supervised-co-training-to-improve","paper_url":"https://arxiv.org/abs/2105.06421v3","paper_title":"Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":36,"model":"LResNet50E-IR","metrics":{"Accuracy (8 emotion)":"53.925"},"uses_additional_data":true,"paper_date":"2020-12-27","paper":"/paper/exploring-emotion-features-and-fusion","paper_url":"https://arxiv.org/abs/2012.13912v1","paper_title":"Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":37,"model":"SL + 20% train (B0)","metrics":{"Accuracy (8 emotion)":"52.46"},"uses_additional_data":false,"paper_date":"2021-05-13","paper":"/paper/using-self-supervised-co-training-to-improve","paper_url":"https://arxiv.org/abs/2105.06421v3","paper_title":"Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":38,"model":"ResEmoteNet","metrics":{"Accuracy (7 emotion)":"72.93"},"uses_additional_data":true,"paper_date":"2024-09-01","paper":"/paper/resemotenet-bridging-accuracy-and-loss","paper_url":"https://arxiv.org/abs/2409.10545v2","paper_title":"ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition","code":"https://github.com/ArnabKumarRoy02/ResEmoteNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":39,"model":"EmoAffectNet","metrics":{"Accuracy (7 emotion)":"66.49"},"uses_additional_data":true,"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},{"rank_in_archive_order":40,"model":"Emotion-GCN","metrics":{"Accuracy (7 emotion)":"66.46"},"uses_additional_data":false,"paper_date":"2021-06-07","paper":"/paper/exploiting-emotional-dependencies-with-graph","paper_url":"https://arxiv.org/abs/2106.03487v2","paper_title":"Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition","code":"https://github.com/PanosAntoniadis/emotion-gcn","n_code_links":1,"syntology":null},{"rank_in_archive_order":41,"model":"FaceBehaviorNet","metrics":{"Accuracy (7 emotion)":"65.40"},"uses_additional_data":true,"paper_date":"2021-05-08","paper":"/paper/distribution-matching-for-heterogeneous-multi","paper_url":"https://arxiv.org/abs/2105.03790v1","paper_title":"Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":42,"model":"Ada-DF","metrics":{"Accuracy (7 emotion)":"65.34"},"uses_additional_data":false,"paper_date":"2023-05-05","paper":"/paper/a-dual-branch-adaptive-distribution-fusion","paper_url":"https://ieeexplore.ieee.org/document/10097033","paper_title":"A Dual-Branch Adaptive Distribution Fusion Framework for Real-World Facial Expression Recognition","code":"https://github.com/taylor-xy0827/Ada-DF","n_code_links":1,"syntology":null},{"rank_in_archive_order":43,"model":"EAC","metrics":{"Accuracy (7 emotion)":"65.32"},"uses_additional_data":false,"paper_date":"2022-07-21","paper":"/paper/learn-from-all-erasing-attention-consistency","paper_url":"https://arxiv.org/abs/2207.10299v2","paper_title":"Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition","code":"https://github.com/zyh-uaiaaaa/erasing-attention-consistency","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":44,"model":"PAENet","metrics":{"Accuracy (7 emotion)":"65.29"},"uses_additional_data":true,"paper_date":"2019-06-10","paper":"/paper/increasingly-packing-multiple-facial","paper_url":"https://dl.acm.org/doi/10.1145/3323873.3325053","paper_title":"Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning","code":"https://github.com/ivclab/CPG","n_code_links":2,"syntology":null},{"rank_in_archive_order":45,"model":"DACL","metrics":{"Accuracy (7 emotion)":"65.20"},"uses_additional_data":false,"paper_date":"2021-01-07","paper":"/paper/facial-expression-recognition-in-the-wild-via","paper_url":"https://openaccess.thecvf.com/content/WACV2021/html/Farzaneh_Facial_Expression_Recognition_in_the_Wild_via_Deep_Attentive_Center_WACV_2021_paper.html","paper_title":"Facial Expression Recognition in the Wild via Deep Attentive Center Loss","code":"https://github.com/amirhfarzaneh/dacl","n_code_links":1,"syntology":null},{"rank_in_archive_order":46,"model":"FerNeXt","metrics":{"Accuracy (7 emotion)":"64.77"},"uses_additional_data":false,"paper_date":"2023-10-20","paper":"/paper/fernext-facial-expression-recognition-using","paper_url":"https://ieeexplore.ieee.org/document/10278345","paper_title":"FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel Attention","code":"https://github.com/OmarEl-Khashab/FerNeXt-Facial-Expression-Recognition-Using-ConvNeXt-with-Channel-Attention","n_code_links":1,"syntology":null},{"rank_in_archive_order":47,"model":"CPG","metrics":{"Accuracy (7 emotion)":"63.57"},"uses_additional_data":true,"paper_date":"2019-10-15","paper":"/paper/compacting-picking-and-growing-for","paper_url":"https://arxiv.org/abs/1910.06562v3","paper_title":"Compacting, Picking and Growing for Unforgetting Continual Learning","code":"https://github.com/ivclab/CPG","n_code_links":2,"syntology":{"n_ran":2,"n_unverified":2,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":48,"model":"Ad-Corre","metrics":{"Accuracy (7 emotion)":"63.36"},"uses_additional_data":false,"paper_date":"2022-03-03","paper":"/paper/ad-corre-adaptive-correlation-based-loss-for","paper_url":"https://ieeexplore.ieee.org/document/9727163","paper_title":"Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild","code":"https://github.com/aliprf/Ad-Corre","n_code_links":1,"syntology":null},{"rank_in_archive_order":49,"model":"CAKE","metrics":{"Accuracy (7 emotion)":"61.7"},"uses_additional_data":false,"paper_date":"2018-07-30","paper":"/paper/cake-compact-and-accurate-k-dimensional","paper_url":"http://arxiv.org/abs/1807.11215v2","paper_title":"CAKE: Compact and Accurate K-dimensional representation of Emotion","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":50,"model":"Facial Motion Prior Network","metrics":{"Accuracy (7 emotion)":"61.52"},"uses_additional_data":false,"paper_date":"2019-02-23","paper":"/paper/facial-motion-prior-networks-for-facial","paper_url":"https://arxiv.org/abs/1902.08788v2","paper_title":"Facial Motion Prior Networks for Facial Expression Recognition","code":"https://github.com/donydchen/FMPN-FER","n_code_links":4,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"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":4,"rows_with_any_sample_ran":3,"distinct_papers_with_graph_line":4,"distinct_papers_with_any_sample_ran":3,"samples_over_distinct_papers":{"n_ran":10,"n_unverified":12,"n_samples":22,"n_pointer_only_licence":1,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":10,"n_unverified":12,"n_samples":22,"n_pointer_only_licence":1,"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"}}}