{"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-learning-for-hand-gesture-recognition-on","title":"Deep Learning for Hand Gesture Recognition on Skeletal Data","arxiv_id":null,"date":"2018-05-15","proceeding":"IEEE FG 2018 2018 5","authors":["Guillaume Devineau","Wang Xi","Jie Yang","Fabien Moutarde"],"abstract":"In this paper, we introduce a new 3D hand gesture recognition approach based on a deep learning model.\r\nWe introduce a new Convolutional Neural Network (CNN) where sequences of hand-skeletal joints’ positions are processed by parallel convolutions; we then investigate the performance of this model on hand gesture sequence classification tasks. Our model only uses hand-skeletal data and no depth image.\r\nExperimental results show that our approach achieves a state-of-the-art performance on a challenging dataset (DHG dataset from the SHREC 2017 3D Shape Retrieval Contest), when compared to other published approaches. Our model achieves a 91.28% classification accuracy for the 14 gesture classes case and an 84.35% classification accuracy for the 28 gesture classes case.","url_abs":"https://hal-mines-paristech.archives-ouvertes.fr/hal-01737771/file/DeepLearning-HandSkeletalGestureRecognition_MINES-ParisTech_FG2018.pdf","url_pdf":"https://hal-mines-paristech.archives-ouvertes.fr/hal-01737771/file/DeepLearning-HandSkeletalGestureRecognition_MINES-ParisTech_FG2018.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-learning-for-hand-gesture-recognition-on","repo_url":"https://github.com/guillaumephd/deep_learning_hand_gesture_recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-shape-retrieval","task_name":"3D Shape Classification"},{"task_slug":"3d-shape-retrieval-1","task_name":"3D Shape Retrieval"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"},{"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"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"skeleton-based-action-recognition","task_name":"Skeleton Based Action Recognition"},{"task_slug":"temporal-information-extraction","task_name":"Temporal Information Extraction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hand-gesture-recognition-on-dhg-14","task":"Hand Gesture Recognition","dataset":"DHG-14","model":"Parallel-Conv","rank_in_archive_order":8,"of":13,"metrics":{"Accuracy":"91.28"},"uses_additional_data":false},{"leaderboard":"/sota/hand-gesture-recognition-on-dhg-28","task":"Hand Gesture Recognition","dataset":"DHG-28","model":"Parallel-Conv","rank_in_archive_order":8,"of":9,"metrics":{"Accuracy":"84.35"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}