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Hand gesture recognition in complex background based on convolutional pose machine and fuzzy gaussian mixture models

Zhang, Tong; Lin, Huifeng; Ju, Zhaojie; Yang, Chenguang

Hand gesture recognition in complex background based on convolutional pose machine and fuzzy gaussian mixture models Thumbnail


Authors

Tong Zhang

Huifeng Lin

Zhaojie Ju



Abstract

© 2020, The Author(s). Hand gesture is one of the most intuitive and natural ways for human to communicate with computers, and it has been widely adopted in many human–computer interaction applications. However, it is still a challenging problem when confronted with complex background, illumination variation and occlusion in real-world scenarios. In this paper, a two-stage hand gesture recognition method is proposed to tackle these problems. At the first stage, hand pose estimation is developed to locate the hand keypoints using the convolutional pose machine, which can effectively localize hand keypoints even in a complex background. At the second stage, the Fuzzy Gaussian mixture models (FGMMs) are tailored to reject the nongesture patterns and classify the gestures based on the estimated hand keypoints. Extensive experiments are conducted to evaluate the performance of the proposed method, and the result demonstrates that the proposed algorithm is effective, robust, and satisfactory in real-time scenarios.

Citation

Zhang, T., Lin, H., Ju, Z., & Yang, C. (2020). Hand gesture recognition in complex background based on convolutional pose machine and fuzzy gaussian mixture models. International Journal of Fuzzy Systems, 22, 1330-1341. https://doi.org/10.1007/s40815-020-00825-w

Journal Article Type Article
Acceptance Date Feb 17, 2020
Online Publication Date Mar 18, 2020
Publication Date Nov 1, 2020
Deposit Date Mar 29, 2020
Publicly Available Date Mar 30, 2020
Journal International Journal of Fuzzy Systems
Print ISSN 1562-2479
Electronic ISSN 2199-3211
Peer Reviewed Peer Reviewed
Volume 22
Pages 1330-1341
DOI https://doi.org/10.1007/s40815-020-00825-w
Keywords Theoretical Computer Science; Computational Theory and Mathematics; Software; Artificial Intelligence; Human–computer interaction; Hand gesture recognition; Convolutional pose machine; Fuzzy Gaussian Mixture Models
Public URL https://uwe-repository.worktribe.com/output/5827689
Additional Information Received: 4 March 2019; Revised: 20 December 2019; Accepted: 17 February 2020; First Online: 18 March 2020

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