Hand Gesture Recognition Using Deep Neural Network and Its Implementation in Augmented Reality

DOI

10.2991/icmmita-16.2016.203How to use a DOI?

Keywords

landmark detection; gesture recognition; object recognition; augmented reality.

Abstract

In this paper, we study the augmented reality and its application in intelligent human-computer interaction. The complex structure in the visual background is a major challenge for gesture segmentation. First, we use a robust skin color segmentation method to preprocess the input image. Second, visual texture features are analyzed and modeled for hand region recognition. Third, finger landmarks are annotated and configured by active shape model. Finally, the hand gestures are recognized and used for enhanced interaction. Experimental results show that the proposed gesture recognition system is robust against various background changes and illumination changes.

Copyright

© 2017, 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  - Huayong Yang
AU  - Xiaoli Lin
PY  - 2017/01
DA  - 2017/01
TI  - Hand Gesture Recognition Using Deep Neural Network and Its Implementation in Augmented Reality
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.203
DO  - 10.2991/icmmita-16.2016.203
ID  - Yang2017/01
ER  -