Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks-Reference-Cited by-同舟云学术

Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks

Published:2020-05-01 Issue:1 Volume:25 Page:57-61
ISSN:2255-8691
Container-title:Applied Computer Systems
language:en
Short-container-title:

Author:

Obaid Falah¹^ORCID,Babadi Amin²^ORCID,Yoosofan Ahmad¹^ORCID

Affiliation:

1. Electrical and Computer Engineering Department , University of Kashan , Kashan , Iran

2. Department of Computer Science , Aalto University , Helsinki , Finland

Abstract

Abstract Deep learning is a new branch of machine learning, which is widely used by researchers in a lot of artificial intelligence applications, including signal processing and computer vision. The present research investigates the use of deep learning to solve the hand gesture recognition (HGR) problem and proposes two models using deep learning architecture. The first model comprises a convolutional neural network (CNN) and a recurrent neural network with a long short-term memory (RNN-LSTM). The accuracy of model achieves up to 82 % when fed by colour channel, and 89 % when fed by depth channel. The second model comprises two parallel convolutional neural networks, which are merged by a merge layer, and a recurrent neural network with a long short-term memory fed by RGB-D. The accuracy of the latest model achieves up to 93 %.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/acss-2020-0007

Reference17 articles.

1. [1] P. Premaratne, “Historical development of hand gesture recognition”, in Human Computer Interaction Using Hand Gestures. Cognitive Science and Technology. Singapore: Springer, 2014, pp. 5–29. https://doi.org/10.1007/978-981-4585-69-9_210.1007/978-981-4585-69-9_2

2. [2] C. S. Chua, H. Guan, Y. K. Ho, “Model-based 3D hand posture estimation from a single 2D image”, Image and Vision computing, vol. 20, no. 3, 2002, pp. 191–202. https://doi.org/10.1016/S0262-8856(01)00094-410.1016/S0262-8856(01)00094-4

3. [3] Z. Lai, Z. Yao, C. Wang, H. Liang, H. Chen, W. Xia, “Fingertips detection and hand gesture recognition based on discrete curve evolution with a kinect sensor”, 2016 Visual Communications and Image Processing (VCIP), IEEE, pp. 1–4, 2016. https://doi.org/10.1109/VCIP.2016.780546410.1109/VCIP.2016.7805464

4. [4] C. Wang, Z. Liu, M. Zhu, J. Zhao, S. C. Chan, “A hand gesture recognition system based on canonical superpixel-graph”, Signal Processing: Image Communication, vol. 58, pp. 87–98, 2017. https://doi.org/10.1016/j.image.2017.06.01510.1016/j.image.2017.06.015

5. [5] A. Joshi, C. Monnier, M. Betke, S. Sclaro, “A random forest approach to segmenting and classifying gestures”, 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), IEEE. pp. 1–7, 2015. https://doi.org/10.1109/FG.2015.716312610.1109/FG.2015.7163126

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