{"url":"/dataset/frgc","name":"FRGC","full_name":"Face Recognition Grand Challenge","description_markdown":"The data for **FRGC** consists of 50,000 recordings divided into training and validation partitions. The training partition is designed for training algorithms and the validation partition is for assessing performance of an approach in a laboratory setting. The validation partition consists of data from 4,003 subject sessions. A subject session is the set of all images of a person taken each time a person's biometric data is collected and consists of four controlled still images, two uncontrolled still images, and one three-dimensional image. The controlled images were taken in a studio setting, are full frontal facial images taken under two lighting conditions and with two facial expressions (smiling and neutral). The uncontrolled images were taken in varying illumination conditions; e.g., hallways, atriums, or outside. Each set of uncontrolled images contains two expressions, smiling and neutral. The 3D image was taken under controlled illumination conditions. The 3D images consist of both a range and a texture image. The 3D images were acquired by a Minolta Vivid 900/910 series sensor.\r\n\r\nSource: [https://www.nist.gov/programs-projects/face-recognition-grand-challenge-frgc](https://www.nist.gov/programs-projects/face-recognition-grand-challenge-frgc)\r\nImage Source: [https://www.researchgate.net/figure/Example-of-images-in-FRGC-20-dataset-The-dataset-consist-of-controlled-images-a-c-as_fig10_285759105](https://www.researchgate.net/figure/Example-of-images-in-FRGC-20-dataset-The-dataset-consist-of-controlled-images-a-c-as_fig10_285759105)","description_withheld":null,"homepage":"https://www.nist.gov/programs-projects/face-recognition-grand-challenge-frgc","introduced_date":"2005-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Overview of the Face Recognition Grand Challenge","first_author":null,"url":"https://doi.org/10.1109/CVPR.2005.268"},"license":{"name":"Custom (non-commercial)","url":"https://cvrl.nd.edu/media/django-summernote/2018-09-19/c7654649-5277-4d8c-b069-483d8ffa3039.pdf"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Clustering","url":"/task/image-clustering","datasets_with_task":"/datasets/task/image-clustering"}],"languages":[],"variants":["FRGC"],"data_loaders":[],"num_papers_in_archive":102,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-clustering-on-frgc","task":"Image Clustering","dataset_variant":"FRGC","rows":3,"metrics":["NMI","Accuracy"],"first_row_in_archive_order":{"model":"DEPICT","paper":"/paper/deep-clustering-via-joint-convolutional","metrics":{"Accuracy":"0.432","NMI":"0.583"},"code_links":[{"title":"herandy/DEPICT","url":"https://github.com/herandy/DEPICT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/deep-clustering-on-the-link-between","title":"Deep clustering: On the link between discriminative models and K-means","date":"2018-10-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-clustering-via-joint-convolutional","title":"Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization","date":"2017-04-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/joint-unsupervised-learning-of-deep","title":"Joint Unsupervised Learning of Deep Representations and Image Clusters","date":"2016-04-13","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}