{"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/analyzing-covariate-influence-on-gender-and","title":"Analyzing Covariate Influence on Gender and Race Prediction from Near-Infrared Ocular Images","arxiv_id":"1805.01912","date":"2018-05-04","proceeding":null,"authors":["Denton Bobeldyk","Arun Ross"],"abstract":"Recent research has explored the possibility of automatically deducing\ninformation such as gender, age and race of an individual from their biometric\ndata. While the face modality has been extensively studied in this regard, the\niris modality less so. In this paper, we first review the medical literature to\nestablish a biological basis for extracting gender and race cues from the iris.\nThen, we demonstrate that it is possible to use simple texture descriptors,\nlike BSIF (Binarized Statistical Image Feature) and LBP (Local Binary\nPatterns), to extract gender and race attributes from an NIR ocular image used\nin a typical iris recognition system. The proposed method predicts gender and\nrace from a single eye image with an accuracy of 86% and 90%, respectively. In\naddition, the following analysis are conducted: (a) the role of different parts\nof the ocular region on attribute prediction; (b) the influence of gender on\nrace prediction, and vice-versa; (c) the impact of eye color on gender and race\nprediction; (d) the impact of image blur on gender and race prediction; (e) the\ngeneralizability of the method across different datasets; and (f) the\nconsistency of prediction performance across the left and right eyes.","url_abs":"http://arxiv.org/abs/1805.01912v4","url_pdf":"http://arxiv.org/pdf/1805.01912v4.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":"analyzing-covariate-influence-on-gender-and","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":"attribute","task_name":"Attribute"},{"task_slug":"iris-recognition","task_name":"Iris Recognition"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}