Sparse Representation for Kinect Based Hand Gesture Recognition System

DOI

10.2991/icaicte.2013.125How to use a DOI?

Keywords

human-computer interaction, hand gesture recognition system, sparse representation, Kinect, auto-encoder neu-ral network computation

Abstract

Hand gesture recognition that has proven a significant factor to directly influence the nonverbal communication between human and computer is becoming a chal-lenging topic in the field of machine vi-sion. This paper aims to propose a novel hand gesture recognition system which applies sparse representation to the Kinect to improve the efficiency of Kinect-based human-computer interac-tion. Auto-encoder neural network com-putation is also utilized to achieve better result. The sparse auto-encoder neural network is versatile and robust in com-plex features learning and computational efficient. Finally, results indicate that our algorithm greatly facilitates the gesture recognition rate up to 95%.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zeke Xu
AU  - Zhenhao Huang
AU  - Zhuoxiong Zhao
AU  - Zhiyuan Li
AU  - Pengsen Huang
PY  - 2013/08
DA  - 2013/08
TI  - Sparse Representation for Kinect Based Hand Gesture Recognition System
BT  - Proceedings of the 2013 International Conference on Advanced ICT and Education
PB  - Atlantis Press
SP  - 610
EP  - 616
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaicte.2013.125
DO  - 10.2991/icaicte.2013.125
ID  - Xu2013/08
ER  -