Angle Based Hand Gesture Recognition Using Graph Convolutional Network

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Angle Based Hand Gesture Recognition Using Graph Convolutional Network 13 Pages Posted: 1 Sep 2022 See all articles by UMME AIMANUMME AIMANaffiliation not provided to SSRNTanvir AhmadJamia Millia Islamia (A Central University) Abstract Hand gesture recognition has attracted huge interest in the areas of autonomous driving, human computer systems, gaming and many others. Skeleton based techniques along with GCN (Graph Convolutional Network) are being popularly used in this field due to the easy access of skeleton joint coordinates and better representation capability of graphs. Hand gestures are much finer and have joints spatially more closely distributed. Simple hand skeleton graphs are not able to capture its complex spatial features. Also, the hand gesture recognition datasets are very small which induces overfitting. To address these challenges, this work first proposes an angle-based hand gesture graph convolutional network. This network introduces two additional kinds of edges in the graph to more explicitly capture the wrist and finger relationship which plays an important role in differentiating most of the gestures. Besides, the features for each skeleton joint are increased from 3 to 25 using a novel technique to extract more semantic correlation and tackle the problem of overfitting. The powerful classification ability of the proposed hand gesture recognition method can be observed in the experimental results that surpass the recognition rates of other competing methods on the challenging SHREC 2017 and DHG 14/28 dataset. Keywords: Hand gesture recognition, CNN, GCN Suggested Citation: Suggested Citation AIMAN, UMME and Ahmad, Tanvir, Angle Based Hand Gesture Recognition Using Graph Convolutional Network. Available at SSRN: https://ssrn.com/abstract=4206743 UMME AIMAN (Contact Author) affiliation not provided to SSRN ( email ) No Address Available Tanvir Ahmad Jamia Millia Islamia (A Central University) ( email ) New Delhi 110025Jamia Millia Islamia (Centre University)India Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 1 Abstract Views 0 PlumX Metrics Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.219 seconds