Abstract
Gesture recognition plays an important role in human computer interaction, but the accuracy is unsatisfactory in complex gestures with slight discrimination. In this paper, a framework facing to recognize complex and similar gestures is presented. In the framework, a parallel connection structure of convolutional neural network (CNN) is designed to extract deep features of complex and similar gestures from RGBD images. Then, a novel feature fusion method is proposed to achieve multi-feature fusion and dimension reduction simultaneously. According to experimental results on American Sign Language (ASL) dataset, the proposed framework reaches 99.042% recognition rate and outperforms current state-of-the-art methods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
