{"url":"/dataset/fg-net","name":"FG-NET","full_name":null,"description_markdown":"FGNet is a dataset for age estimation and face recognition across ages. It is composed of a total of 1,002 images of 82 people with age range from 0 to 69 and an age gap up to 45 years\r\n\r\nSource: [Large age-gap face verification by feature injection in deep networks](https://arxiv.org/abs/1602.06149)\r\nImage Source: [https://www.researchgate.net/figure/Sample-images-from-the-FG-NET-Aging-database_fig1_220057621](https://www.researchgate.net/figure/Sample-images-from-the-FG-NET-Aging-database_fig1_220057621)","description_withheld":null,"homepage":"https://yanweifu.github.io/FG_NET_data/","introduced_date":"2002-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Toward Automatic Simulation of Aging Effects on Face Images","first_author":null,"url":"https://doi.org/10.1109/34.993553"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Age Estimation","url":"/task/age-estimation","datasets_with_task":"/datasets/task/age-estimation"},{"name":"Age-Invariant Face Recognition","url":"/task/age-invariant-face-recognition","datasets_with_task":"/datasets/task/age-invariant-face-recognition"}],"languages":[],"variants":["FG-NET","FGNET"],"data_loaders":[{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/fgnet-dataset","frameworks":["tf","pytorch"]}],"num_papers_in_archive":54,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/age-estimation-on-fgnet","task":"Age Estimation","dataset_variant":"FGNET","rows":8,"metrics":["MAE"],"first_row_in_archive_order":{"model":"MWR","paper":"/paper/moving-window-regression-a-novel-approach-to","metrics":{"MAE":"2.23"},"code_links":[{"title":"nhshin-mcl/mwr","url":"https://github.com/nhshin-mcl/mwr"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/age-invariant-face-recognition-on-fg-net","task":"Age-Invariant Face Recognition","dataset_variant":"FG-NET","rows":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"MTLFace","paper":"/paper/when-age-invariant-face-recognition-meets","metrics":{"Accuracy":"94.78%"},"code_links":[{"title":"Hzzone/MTLFace","url":"https://github.com/Hzzone/MTLFace"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/moving-window-regression-a-novel-approach-to","title":"Moving Window Regression: A Novel Approach to Ordinal Regression","date":"2022-03-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":0,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/when-age-invariant-face-recognition-meets","title":"When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework","date":"2021-03-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/c3ae-exploring-the-limits-of-compact-model","title":"C3AE: Exploring the Limits of Compact Model for Age Estimation","date":"2019-04-10","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/bridgenet-a-continuity-aware-probabilistic","title":"BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation","date":"2019-04-06","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/look-across-elapse-disentangled","title":"Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition","date":"2018-09-02","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/facial-aging-and-rejuvenation-by-conditional","title":"Facial Aging and Rejuvenation by Conditional Multi-Adversarial Autoencoder with Ordinal Regression","date":"2018-04-08","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/deep-regression-forests-for-age-estimation","title":"Deep Regression Forests for Age Estimation","date":"2017-12-19","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/deep-expectation-of-real-and-apparent-age","title":"Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks","date":"2016-08-10","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":13,"samples_ran":0,"samples_unverified":13,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":2,"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."}