@inproceedings{85951667fc0b45dd9b96472b756cff97,
title = "Dynamic hand gesture recognition based on 3D convolutional neural network models",
abstract = "Hand gesture is a natural communication method which could be used to create a more convenient interface for human-robot interaction. In this study, we use the simplest laptop camera as an input sensor. We designed a 3D hand gesture recognition model. The model is trained with the Jester dataset. After being trained about one day in a MacBook Pro (i5 2.3GHz), the model reached an average accuracy of 90\%. We built a web application that implements the hand gesture recognition system and provides the recognition service to users.",
keywords = "3D CNN, Convolution neural network, Deep learning, Hand gesture recognition, Neural network",
author = "Wenjin Zhang and Jiacun Wang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE International Conference on Networking, Sensing and Control, ICNSC 2019 ; Conference date: 09-05-2019 Through 11-05-2019",
year = "2019",
month = may,
doi = "10.1109/ICNSC.2019.8743159",
language = "英语",
series = "Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "224--229",
editor = "Haibin Zhu and Jiacun Wang and MengChu Zhou",
booktitle = "Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019",
address = "美国",
}