TY - GEN
T1 - An Incremental Learning Framework for Skeletal-based Hand Gesture Recognition with Leap Motion
AU - Li, Jie
AU - Zhong, Junpei
AU - Chen, Fei
AU - Yang, Chenguang
N1 - Funding Information:
This work was partially supported by National Nature Science Foundation (NSFC) under Grants 61861136009 and 61811530281. J. Li and C. Yang are with the Key Laboratory of Autonomous Systems and Networked Control, School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China. J. Zhong is with School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK. F. Chen is with the Department of Advanced Robotics, Istituto Italiano di Tecnologia, via Morego, 30, 16163 Genova, Italy. C. Yang is with Bristol Robotics Laboratory, University of the West of England, Bristol, BS16 1QY, UK. Corresponding author is C. Yang. Email: [email protected].
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Hand gesture recognition has become the focus of researchers lately because of its manifold applications in various fields. Leap Motion (LM) is a device to obtain useful and accurate information of the hand action, which is suitable for collecting the three-dimensional (3D) human hand gesture. In this paper, a novel framework which consists of an incremental learning (IL) algorithm without deep structure is proposed and applied to hand gestures classification that explicitly aimed to the LM data. The same datasets are used to train the proposed framework and the conventional Long Short Term Memory Recurrent Neural Network (LSTM-RNN). Due to the structural advantage of the proposed model, the recognition performance is improved distinctly in robustness and training time than the LSTM network. Moreover, convincing experiment results are given to illustrate that the solution is more efficient in static gesture classification.
AB - Hand gesture recognition has become the focus of researchers lately because of its manifold applications in various fields. Leap Motion (LM) is a device to obtain useful and accurate information of the hand action, which is suitable for collecting the three-dimensional (3D) human hand gesture. In this paper, a novel framework which consists of an incremental learning (IL) algorithm without deep structure is proposed and applied to hand gestures classification that explicitly aimed to the LM data. The same datasets are used to train the proposed framework and the conventional Long Short Term Memory Recurrent Neural Network (LSTM-RNN). Due to the structural advantage of the proposed model, the recognition performance is improved distinctly in robustness and training time than the LSTM network. Moreover, convincing experiment results are given to illustrate that the solution is more efficient in static gesture classification.
UR - http://www.scopus.com/inward/record.url?scp=85084317040&partnerID=8YFLogxK
U2 - 10.1109/CYBER46603.2019.9066761
DO - 10.1109/CYBER46603.2019.9066761
M3 - Conference article published in proceeding or book
AN - SCOPUS:85084317040
T3 - 9th IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2019
SP - 13
EP - 18
BT - 9th IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2019
Y2 - 29 July 2019 through 2 August 2019
ER -