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Sensors 2019, 19(2), 239; https://doi.org/10.3390/s19020239

MFA-Net: Motion Feature Augmented Network for Dynamic Hand Gesture Recognition from Skeletal Data

1
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2
AI Lab, Bytedance Inc., Beijing 100086, China
3
Beijing Huajie IMI Technology Co., Ltd, Beijing 100193, China
This paper is an extended version of our paper published in: Chen, X.; Guo, H.; Wang, G.; Zhang, L. Motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition. In Proceedings of the IEEE International Conference on Image Processing (ICIP), Beijing, China, 17–20 September 2017.
*
Author to whom correspondence should be addressed.
Received: 6 December 2018 / Revised: 2 January 2019 / Accepted: 7 January 2019 / Published: 10 January 2019
(This article belongs to the Section Intelligent Sensors)
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Abstract

Dynamic hand gesture recognition has attracted increasing attention because of its importance for human–computer interaction. In this paper, we propose a novel motion feature augmented network (MFA-Net) for dynamic hand gesture recognition from skeletal data. MFA-Net exploits motion features of finger and global movements to augment features of deep network for gesture recognition. To describe finger articulated movements, finger motion features are extracted from the hand skeleton sequence via a variational autoencoder. Global motion features are utilized to represent the global movements of hand skeleton. These motion features along with the skeleton sequence are then fed into three branches of a recurrent neural network (RNN), which augment the motion features for RNN and improve the classification performance. The proposed MFA-Net is evaluated on two challenging skeleton-based dynamic hand gesture datasets, including DHG-14/28 dataset and SHREC’17 dataset. Experimental results demonstrate that our proposed method achieves comparable performance on DHG-14/28 dataset and better performance on SHREC’17 dataset when compared with start-of-the-art methods. View Full-Text
Keywords: skeleton; gesture recognition; recurrent neural networks; feature augmentation skeleton; gesture recognition; recurrent neural networks; feature augmentation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Chen, X.; Wang, G.; Guo, H.; Zhang, C.; Wang, H.; Zhang, L. MFA-Net: Motion Feature Augmented Network for Dynamic Hand Gesture Recognition from Skeletal Data. Sensors 2019, 19, 239.

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