{"url":"/dataset/ufpr-periocular","name":"UFPR-Periocular","full_name":null,"description_markdown":"The UFPR-Periocular dataset has 16,830 images of both eyes (33,660 cropped images of each eye) from 1,122 subjects (2,244 classes).\r\n\r\nAll the images were captured by the participant using their own smartphone through a mobile application (app) developed by the authors.\r\nThere are 15 samples from each subject's eye, obtained in 3 sessions (5 images per session) with a minimum interval of 8 hours between the sessions.\r\n\r\nThe images were collected from June 2019 to January 2020 and have several resolutions varying from 360×160 to 1862×1008 pixels – depending on the mobile device used to capture the image. In total, the dataset has images captured from 196 different mobile devices.\r\n\r\nEach subject captured their images using the same device model. This dataset's main intra- and inter-class variability are caused by lighting variation, occlusion, specular reflection, blur, motion blur, eyeglasses, off-angle, eye-gaze, makeup, and facial expression.\r\n\r\nThe authors manually annotated the eye corner of all images with 4 points (inside and outside eye corners) and used it to normalize the periocular region regarding scale and rotation. All the original and cropped periocular images, eye-corner annotations, and experimental protocol files are publicly available for the research community (upon request).\r\n\r\nThe paper contains information about images' distributions by gender, age, resolution, and other experiments' details and benchmarks.\r\n\r\nSource: [A new periocular dataset collected by mobile devices in unconstrained scenarios](https://doi.org/10.1038/s41598-022-22811-y)","description_withheld":null,"homepage":"https://web.inf.ufpr.br/vri/databases/ufpr-periocular/","introduced_date":"2022-10-26","introduced_date_note":null,"introduced_by":{"paper":"/paper/ufpr-periocular-a-periocular-dataset","title":"A New Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios","first_author":"Luiz A. Zanlorensi","url":null},"license":{"name":"Research Only","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Mobile Periocular Recognition","url":"/task/mobile-periocular-recognition","datasets_with_task":"/datasets/task/mobile-periocular-recognition"},{"name":"Iris Recognition","url":"/task/iris-recognition","datasets_with_task":"/datasets/task/iris-recognition"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["UFPR-Periocular"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"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."}