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Facial Expression Recognition (FER) datasets
archive 2025-07-2829 datasets carry the task tag "Facial Expression Recognition (FER)" (the task itself: Facial Expression Recognition (FER)), ordered by the archive's paper count. Page 1 of 1: 29 shown of 29. Facet routes are this site's own (the archive records the tag string, not a page).
The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.
Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets
All 12,172
Question Answering 413Semantic Segmentation 347Object Detection 332Image Classification 283Classification 222Text Classification 167Language Modelling 166Text Generation 162Visual Question Answering (VQA) 144Named Entity Recognition (NER) 130Pose Estimation 124Anomaly Detection 119Action Recognition 115Instance Segmentation 111Sentiment Analysis 104Text Summarization 98Domain Adaptation 96Speech Recognition 96Reading Comprehension 95Information Retrieval 932D Object Detection 92Image Retrieval 87Machine Translation 82Relation Extraction 822D Semantic Segmentation 81Natural Language Inference 81Image Captioning 79Image Generation 79Depth Estimation 77Node Classification 75Natural Language Understanding 72Autonomous Driving 70Code Generation 70Object Tracking 693D Object Detection 67Face Recognition 67Link Prediction 67Person Re-Identification 65Data Augmentation 63Multi-Task Learning 593D Reconstruction 58Common Sense Reasoning 56Recommendation Systems 56Video Understanding 56Optical Character Recognition (OCR) 55Graph Classification 54Abstractive Text Summarization 52Emotion Recognition 52Medical Image Segmentation 52Word Embeddings 52Facial Expression Recognition (FER) 29
Facial Expression Recognition (FER) datasets 1–29 of 29
AffectNet (burak yılmaz)
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…
323 papers · 4 benchmarks
CK+ (Extended Cohn-Kanade dataset)
The Extended Cohn-Kanade (CK+) dataset contains 593 video sequences from a total of 123 different subjects, ranging from 18 to 50 years of age with a variety of genders and heritage.
238 papers · 2 benchmarks
RAF-DB (Real-world Affective Faces)
The Real-world Affective Faces Database (RAF-DB) is a dataset for facial expression.
172 papers · 3 benchmarks
FER2013 (Facial Expression Recognition 2013 Dataset)
Fer2013 contains approximately 30,000 facial RGB images of different expressions with size restricted to 48×48, and the main labels of it can be divided into 7 types: 0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral.
168 papers · 5 benchmarks
DISFA (Denver Intensity of Spontaneous Facial Action)
The Denver Intensity of Spontaneous Facial Action (DISFA) dataset consists of 27 videos of 4844 frames each, with 130,788 images in total.
148 papers · 3 benchmarks
Aff-Wild2 is a large-scale in-the-wild database and an extension of the Aff-Wild dataset for affect recognition.
142 papers · 2 benchmarks
FER+ (Face Expression Recognition Plus dataset)
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.
124 papers · 3 benchmarks
The BP4D-Spontaneous dataset is a 3D video database of spontaneous facial expressions in a diverse group of young adults.
104 papers · 3 benchmarks
JAFFE (Japanese Female Facial Expression)
The JAFFE dataset consists of 213 images of different facial expressions from 10 different Japanese female subjects.
93 papers · 4 benchmarks
RaFD (Radboud Faces Database)
The Radboud Faces Database (RaFD) is a set of pictures of 67 models (both adult and children, males and females) displaying 8 emotional expressions.
81 papers · 2 benchmarks
Oulu-CASIA (Oulu-CASIA NIR&VIS facial expression database)
The Oulu-CASIA NIR&VIS facial expression database consists of six expressions (surprise, happiness, sadness, anger, fear and disgust) from 80 people between 23 and 58 years old.
80 papers · 4 benchmarks
MMI (MMI Facial Expression Database)
The MMI Facial Expression Database consists of over 2900 videos and high-resolution still images of 75 subjects.
65 papers · 1 benchmark
SFEW (Static Facial Expression in the Wild)
The Static Facial Expressions in the Wild (SFEW) dataset is a dataset for facial expression recognition.
61 papers · 1 benchmark
DFEW (Dynamic Facial Expression in the Wild)
Recently, facial expression recognition (FER) in the wild has gained a lot of researchers’ attention because it is a valuable topic to enable the FER techniques to move from the laboratory to the real applications.
45 papers · 0 benchmarks
ExpW (Expression in-the-Wild)
The Expression in-the-Wild (ExpW) dataset is for facial expression recognition and contains 91,793 faces manually labeled with expressions.
41 papers · 1 benchmark
Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos.
32 papers · 0 benchmarks
MAFW is a large-scale, multi-modal, compound affective database for dynamic facial expression recognition in the wild.
31 papers · 2 benchmarks
CREMA-D is an emotional multimodal actor data set of 7,442 original clips from 91 actors.
28 papers · 7 benchmarks
RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song)
The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) contains 7,356 files (total size: 24.8 GB).
27 papers · 6 benchmarks
DAiSEE is a multi-label video classification dataset comprising of 9,068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration "in the wild".
17 papers · 1 benchmark
4DFAB is a large scale database of dynamic high-resolution 3D faces which consists of recordings of 180 subjects captured in four different sessions spanning over a five-year period (2012 - 2017), resulting in a total of over 1,800,000 3D…
14 papers · 0 benchmarks
Acted Facial Expressions In The Wild (AFEW) is a dynamic temporal facial expressions data corpus consisting of close to real world environment extracted from movie
8 papers · 1 benchmark
FERG (Facial Expression Research Group Database)
FERG is a database of cartoon characters with annotated facial expressions containing 55,769 annotated face images of six characters.
8 papers · 1 benchmark
Jiyoung Lee, Seungryong Kim, Sunok Kim, Jungin Park, Kwanghoon Sohn; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp.
5 papers · 2 benchmarks
FEAFA+ is a dataset for Facial expression analysis and 3D Facial animation.
3 papers · 0 benchmarks
SAVEE (Surrey Audio-Visual Expressed Emotion)
The Surrey Audio-Visual Expressed Emotion (SAVEE) dataset was recorded as a pre-requisite for the development of an automatic emotion recognition system.
3 papers · 1 benchmark
MH-FED (Meta Human Facial Expression Dataset)
This dataset provides a collection of 162K images and 70 Videos of Meta-Humans.
1 paper · 0 benchmarks
Description: 4,458 People - 3D Facial Expressions Recognition Data.
0 papers · 0 benchmarks
This dataset consists of 600+ items of faces with different emotions and mixed races that are ready to use for optimizing the accuracy of computer vision models.
0 papers · 0 benchmarks
Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.