{"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/deep-fisher-discriminant-learning-for-mobile","title":"Deep Fisher Discriminant Learning for Mobile Hand Gesture Recognition","arxiv_id":"1707.03692","date":"2017-07-12","proceeding":null,"authors":["Chunyu Xie","Ce Li","Baochang Zhang","Chen Chen","Jungong Han"],"abstract":"Gesture recognition is a challenging problem in the field of biometrics. In\nthis paper, we integrate Fisher criterion into Bidirectional Long-Short Term\nMemory (BLSTM) network and Bidirectional Gated Recurrent Unit (BGRU),thus\nleading to two new deep models termed as F-BLSTM and F-BGRU. BothFisher\ndiscriminative deep models can effectively classify the gesture based on\nanalyzing the acceleration and angular velocity data of the human gestures.\nMoreover, we collect a large Mobile Gesture Database (MGD) based on the\naccelerations and angular velocities containing 5547 sequences of 12 gestures.\nExtensive experiments are conducted to validate the superior performance of the\nproposed networks as compared to the state-of-the-art BLSTM and BGRU on MGD\ndatabase and two benchmark databases (i.e. BUAA mobile gesture and SmartWatch\ngesture).","url_abs":"http://arxiv.org/abs/1707.03692v1","url_pdf":"http://arxiv.org/pdf/1707.03692v1.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":"deep-fisher-discriminant-learning-for-mobile","repo_url":"https://github.com/chriswegmann/drone_steering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"},{"task_slug":"hand-gesture-recognition","task_name":"Hand Gesture Recognition"},{"task_slug":"hand-gesture-recognition-1","task_name":"Hand-Gesture Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hand-gesture-recognition-on-buaa","task":"Hand Gesture Recognition","dataset":"BUAA","model":"F-BGRU","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"99.25"},"uses_additional_data":false},{"leaderboard":"/sota/hand-gesture-recognition-on-mgb","task":"Hand Gesture Recognition","dataset":"MGB","model":"F-BLSTM","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"98.04"},"uses_additional_data":false},{"leaderboard":"/sota/hand-gesture-recognition-on-smartwatch","task":"Hand Gesture Recognition","dataset":"SmartWatch","model":"F-BGRU","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"97.4"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}