{"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/gender-classification-from-iris-texture","title":"Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features","arxiv_id":"1905.00372","date":"2019-05-01","proceeding":null,"authors":["Juan Tapia","Claudia Arellano"],"abstract":"Soft biometric information such as gender can contribute to many applications\nlike as identification and security. This paper explores the use of a Binary\nStatistical Features (BSIF) algorithm for classifying gender from iris texture\nimages captured with NIR sensors. It uses the same pipeline for iris\nrecognition systems consisting of iris segmentation, normalisation and then\nclassification. Experiments show that applying BSIF is not straightforward\nsince it can create artificial textures causing misclassification. In order to\novercome this limitation, a new set of filters was trained from eye images and\ndifferent sized filters with padding bands were tested on a subject-disjoint\ndatabase. A Modified-BSIF (MBSIF) method was implemented. The latter achieved\nbetter gender classification results (94.6\\% and 91.33\\% for the left and right\neye respectively). These results are competitive with the state of the art in\ngender classification. In an additional contribution, a novel gender labelled\ndatabase was created and it will be available upon request.","url_abs":"http://arxiv.org/abs/1905.00372v1","url_pdf":"http://arxiv.org/pdf/1905.00372v1.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":"gender-classification-from-iris-texture","repo_url":"https://github.com/Developer-Y/cs-video-courses","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"gender-classification","task_name":"Gender Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"iris-recognition","task_name":"Iris Recognition"},{"task_slug":"iris-segmentation","task_name":"Iris 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}