{"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/11k-hands-gender-recognition-and-biometric","title":"11K Hands: Gender recognition and biometric identification using a large dataset of hand images","arxiv_id":"1711.04322","date":"2017-11-12","proceeding":null,"authors":["Mahmoud Afifi"],"abstract":"The human hand possesses distinctive features which can reveal gender\ninformation. In addition, the hand is considered one of the primary biometric\ntraits used to identify a person. In this work, we propose a large dataset of\nhuman hand images (dorsal and palmar sides) with detailed ground-truth\ninformation for gender recognition and biometric identification. Using this\ndataset, a convolutional neural network (CNN) can be trained effectively for\nthe gender recognition task. Based on this, we design a two-stream CNN to\ntackle the gender recognition problem. This trained model is then used as a\nfeature extractor to feed a set of support vector machine classifiers for the\nbiometric identification task. We show that the dorsal side of hand images,\ncaptured by a regular digital camera, convey effective distinctive features\nsimilar to, if not better, those available in the palmar hand images. To\nfacilitate access to the proposed dataset and replication of our experiments,\nthe dataset, trained CNN models, and Matlab source code are available at\n(https://goo.gl/rQJndd).","url_abs":"http://arxiv.org/abs/1711.04322v9","url_pdf":"http://arxiv.org/pdf/1711.04322v9.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":"11k-hands-gender-recognition-and-biometric","repo_url":"https://github.com/mahmoudnafifi/11K-Hands","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"animal-pose-estimation","task_name":"Animal Pose Estimation"},{"task_slug":"object-detection","task_name":"Object Detection"}],"methods":[],"datasets_introduced":[{"slug":"11k-hands","name":"11k Hands","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.04322","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}