{"url":"/dataset/ferg","name":"FERG","full_name":"Facial Expression Research Group Database","description_markdown":"**FERG** is a database of cartoon characters with annotated facial expressions containing 55,769 annotated face images of six characters. The images for each character are grouped into 7 types of cardinal expressions, viz. anger, disgust, fear, joy, neutral, sadness and surprise.\n\nSource: [VGAN-Based Image Representation Learningfor Privacy-Preserving Facial Expression Recognition](https://arxiv.org/abs/1803.07100)\nImage Source: [http://grail.cs.washington.edu/projects/deepexpr/ferg-2d-db.html](http://grail.cs.washington.edu/projects/deepexpr/ferg-2d-db.html)","description_withheld":null,"homepage":"http://grail.cs.washington.edu/projects/deepexpr/ferg-2d-db.html","introduced_date":"2016-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Modeling Stylized Character Expressions via Deep Learning","first_author":null,"url":"https://doi.org/10.1007/978-3-319-54184-6_9"},"license":null,"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"}],"languages":[],"variants":["FERG"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/facial-expression-recognition-on-ferg","task":"Facial Expression Recognition (FER)","dataset_variant":"FERG","rows":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"DeepEmotion","paper":"/paper/deep-emotion-facial-expression-recognition","metrics":{"Accuracy":"99.3"},"code_links":[{"title":"omarsayed7/Deep-Emotion","url":"https://github.com/omarsayed7/Deep-Emotion"},{"title":"KLT20/Realtime-Face-Emotion-Recognition","url":"https://github.com/KLT20/Realtime-Face-Emotion-Recognition"},{"title":"kaushal-k/Deep-Emotion","url":"https://github.com/kaushal-k/Deep-Emotion"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"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/deep-emotion-facial-expression-recognition","title":"Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network","date":"2019-02-04","rows_on_this_dataset":1,"code_links":3,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}