{"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/iris-recognition-with-image-segmentation","title":"Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks","arxiv_id":"1901.01028","date":"2019-01-04","proceeding":null,"authors":["Daniel Kerrigan","Mateusz Trokielewicz","Adam Czajka","Kevin Bowyer"],"abstract":"This paper offers three new, open-source, deep learning-based iris\nsegmentation methods, and the methodology how to use irregular segmentation\nmasks in a conventional Gabor-wavelet-based iris recognition. To train and\nvalidate the methods, we used a wide spectrum of iris images acquired by\ndifferent teams and different sensors and offered publicly, including data\ntaken from CASIA-Iris-Interval-v4, BioSec, ND-Iris-0405, UBIRIS,\nWarsaw-BioBase-Post-Mortem-Iris v2.0 (post-mortem iris images), and\nND-TWINS-2009-2010 (iris images acquired from identical twins). This varied\ntraining data should increase the generalization capabilities of the proposed\nsegmentation techniques. In database-disjoint training and testing, we show\nthat deep learning-based segmentation outperforms the conventional (OSIRIS)\nsegmentation in terms of Intersection over Union calculated between the\nobtained results and manually annotated ground-truth. Interestingly, the\nGabor-based iris matching is not always better when deep learning-based\nsegmentation is used, and is on par with the method employing Daugman's based\nsegmentation.","url_abs":"http://arxiv.org/abs/1901.01028v1","url_pdf":"http://arxiv.org/pdf/1901.01028v1.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":"iris-recognition-with-image-segmentation","repo_url":"https://github.com/CVRL/iris-recognition-OTS-DNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"iris-recognition","task_name":"Iris Recognition"},{"task_slug":"iris-segmentation","task_name":"Iris Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic 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}