{"url":"/dataset/morph","name":"MORPH","full_name":null,"description_markdown":"**MORPH** is a facial age estimation dataset, which contains 55,134 facial images of 13,617 subjects ranging from 16 to 77 years old.\r\n\r\nSource: [Deep Ordinal Regression Forests](https://arxiv.org/abs/2008.03077)\r\nImage Source: [https://uncw.edu/oic/tech/morph.html](https://uncw.edu/oic/tech/morph.html)","description_withheld":null,"homepage":"https://uncw.edu/oic/tech/morph.html","introduced_date":"2006-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"MORPH: A Longitudinal Image Database of Normal Adult Age-Progression","first_author":null,"url":"https://doi.org/10.1109/FGR.2006.78"},"license":{"name":"Commercial","url":"https://ebill.uncw.edu/C20231_ustores/web/product_detail.jsp?PRODUCTID=8"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Face Recognition","url":"/task/face-recognition","datasets_with_task":"/datasets/task/face-recognition"},{"name":"Age Estimation","url":"/task/age-estimation","datasets_with_task":"/datasets/task/age-estimation"},{"name":"Fairness","url":"/task/fairness","datasets_with_task":"/datasets/task/fairness"},{"name":"Facial Attribute Classification","url":"/task/facial-attribute-classification","datasets_with_task":"/datasets/task/facial-attribute-classification"},{"name":"Age-Invariant Face Recognition","url":"/task/age-invariant-face-recognition","datasets_with_task":"/datasets/task/age-invariant-face-recognition"},{"name":"Few-shot Age Estimation","url":"/task/few-shot-age-estimation","datasets_with_task":"/datasets/task/few-shot-age-estimation"}],"languages":[],"variants":["MORPH","MORPH Album2","MORPH album2 (Caucasian)"],"data_loaders":[{"repo":"https://github.com/Kimocamp/MORPH","url":"https://github.com/Kimocamp/MORPH","frameworks":[]}],"num_papers_in_archive":180,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/age-estimation-on-morph-album2-caucasian","task":"Age Estimation","dataset_variant":"MORPH album2 (Caucasian)","rows":11,"metrics":["MAE"],"first_row_in_archive_order":{"model":"MWR","paper":"/paper/moving-window-regression-a-novel-approach-to","metrics":{"MAE":"2.13"},"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-estimation-on-morph-album2","task":"Age Estimation","dataset_variant":"MORPH Album2","rows":9,"metrics":["MAE","CS"],"first_row_in_archive_order":{"model":"Hierarchical Attention-based Age Estimation (RS)","paper":"/paper/hierarchical-attention-based-age-estimation","metrics":{"MAE":"1.13"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/age-invariant-face-recognition-on-morph","task":"Age-Invariant Face Recognition","dataset_variant":"MORPH Album2","rows":3,"metrics":["Rank-1 Recognition Rate"],"first_row_in_archive_order":{"model":"AIM + CAFR","paper":"/paper/look-across-elapse-disentangled","metrics":{"Rank-1 Recognition Rate":"99.65%"},"code_links":[{"title":"ZhaoJ9014/High_Performance_Face_Recognition","url":"https://github.com/ZhaoJ9014/High_Performance_Face_Recognition"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/face-recognition-on-morph","task":"Face Recognition","dataset_variant":"MORPH","rows":3,"metrics":["FNMR [%] @ 10-3 FMR"],"first_row_in_archive_order":{"model":"PIC - ArcFace","paper":"/paper/pic-score-probabilistic-interpretable","metrics":{"FNMR [%] @ 10-3 FMR":"0.05"},"code_links":[{"title":"Faceplugin-ltd/FaceRecognition-Android","url":"https://github.com/Faceplugin-ltd/FaceRecognition-Android"},{"title":"FaceOnLive/Face-Recognition-SDK-Android","url":"https://github.com/FaceOnLive/Face-Recognition-SDK-Android"},{"title":"pterhoer/optimalmatchingconfidence","url":"https://github.com/pterhoer/optimalmatchingconfidence"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/few-shot-age-estimation-on-morph-album2","task":"Few-shot Age Estimation","dataset_variant":"MORPH Album2","rows":2,"metrics":["MAE","MAE (2 shot)","MAE (4 shot)","MAE (8 shot)","MAE (16 shot)"],"first_row_in_archive_order":{"model":"OrdinalCLIP","paper":"/paper/ordinalclip-learning-rank-prompts-for","metrics":{"MAE":"4.94","MAE (16 shot)":"3.07","MAE (2 shot)":"4.36","MAE (4 shot)":"3.55","MAE (8 shot)":"3.31"},"code_links":[{"title":"xk-huang/OrdinalCLIP","url":"https://github.com/xk-huang/OrdinalCLIP"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/age-estimation-on-morph","task":"Age Estimation","dataset_variant":"MORPH","rows":1,"metrics":["MAE"],"first_row_in_archive_order":{"model":"CMAAE-OR","paper":"/paper/facial-aging-and-rejuvenation-by-conditional","metrics":{"MAE":"1.48"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/facial-attribute-classification-on-morph","task":"Facial Attribute Classification","dataset_variant":"MORPH","rows":1,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"Neighbour Learning","paper":"/paper/deep-generative-views-to-mitigate-gender","metrics":{"Accuracy (%)":"96.41"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/fairness-on-morph","task":"Fairness","dataset_variant":"MORPH","rows":1,"metrics":["Degree of Bias (DoB)"],"first_row_in_archive_order":{"model":"Neighbour Learning","paper":"/paper/deep-generative-views-to-mitigate-gender","metrics":{"Degree of Bias (DoB)":"6.26"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/pic-score-probabilistic-interpretable","title":"PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric (Face) Recognition","date":"2022-11-22","rows_on_this_dataset":3,"code_links":3,"syntology":null},{"paper":"/paper/deep-generative-views-to-mitigate-gender","title":"Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups","date":"2022-08-17","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/metaage-meta-learning-personalized-age","title":"MetaAge: Meta-Learning Personalized Age Estimators","date":"2022-07-12","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/ordinalclip-learning-rank-prompts-for","title":"OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression","date":"2022-06-06","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"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":2,"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/learning-to-prompt-for-vision-language-models","title":"Learning to Prompt for Vision-Language Models","date":"2021-09-02","rows_on_this_dataset":1,"code_links":18,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":3,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-probabilistic-ordinal-embeddings-for","title":"Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression","date":"2021-03-25","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":8,"samples_unverified":0,"pointer_only_for_licence":8,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hierarchical-attention-based-age-estimation","title":"Hierarchical Attention-based Age Estimation and Bias Estimation","date":"2021-03-17","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/deep-repulsive-clustering-of-ordered-data","title":"Deep Repulsive Clustering of Ordered Data Based on Order-Identity Decomposition","date":"2021-01-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/adaptive-variance-based-label-distribution","title":"Adaptive Variance Based Label Distribution Learning For Facial Age Estimation","date":"2020-08-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/learning-expectation-of-label-distribution","title":"Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation","date":"2020-07-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/order-learning-and-its-application-to-age","title":"Order Learning and Its Application to Age Estimation","date":"2020-05-01","rows_on_this_dataset":1,"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/consistent-rank-logits-for-ordinal-regression","title":"Rank consistent ordinal regression for neural networks with application to age estimation","date":"2019-01-20","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/orthogonal-deep-features-decomposition-for","title":"Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition","date":"2018-10-17","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":2,"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":1,"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/quantifying-facial-age-by-posterior-of-age","title":"Quantifying Facial Age by Posterior of Age Comparisons","date":"2017-08-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/label-distribution-learning-forests","title":"Label Distribution Learning Forests","date":"2017-02-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-label-distribution-learning-with-label","title":"Deep Label Distribution Learning with Label Ambiguity","date":"2016-11-06","rows_on_this_dataset":2,"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":5,"samples_harvested":37,"samples_ran":12,"samples_unverified":25,"pointer_only_for_licence":8,"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."}