{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-free-iris-segmentation-revisited-a","title":"Learning-Free Iris Segmentation Revisited: A First Step Toward Fast Volumetric Operation Over Video Samples","arxiv_id":"1901.01575","date":"2019-01-06","proceeding":null,"authors":["Jeffery Kinnison","Mateusz Trokielewicz","Camila Carballo","Adam Czajka","Walter Scheirer"],"abstract":"Subject matching performance in iris biometrics is contingent upon fast,\nhigh-quality iris segmentation. In many cases, iris biometrics acquisition\nequipment takes a number of images in sequence and combines the segmentation\nand matching results for each image to strengthen the result. To date,\nsegmentation has occurred in 2D, operating on each image individually. But such\nmethodologies, while powerful, do not take advantage of potential gains in\nperformance afforded by treating sequential images as volumetric data. As a\nfirst step in this direction, we apply the Flexible Learning-Free\nReconstructoin of Neural Volumes (FLoRIN) framework, an open source\nsegmentation and reconstruction framework originally designed for neural\nmicroscopy volumes, to volumetric segmentation of iris videos. Further, we\nintroduce a novel dataset of near-infrared iris videos, in which each subject's\npupil rapidly changes size due to visible-light stimuli, as a test bed for\nFLoRIN. We compare the matching performance for iris masks generated by FLoRIN,\ndeep-learning-based (SegNet), and Daugman's (OSIRIS) iris segmentation\napproaches. We show that by incorporating volumetric information, FLoRIN\nachieves a factor of 3.6 to an order of magnitude increase in throughput with\nonly a minor drop in subject matching performance. We also demonstrate that\nFLoRIN-based iris segmentation maintains this speedup on low-resource hardware,\nmaking it suitable for embedded biometrics systems.","url_abs":"http://arxiv.org/abs/1901.01575v1","url_pdf":"http://arxiv.org/pdf/1901.01575v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-free-iris-segmentation-revisited-a","repo_url":"https://github.com/jeffkinnison/florin-iris","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"iris-segmentation","task_name":"Iris Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}