@inproceedings{a4607ee49acc4ed88d0fac1364da861b,
title = "3D smiling facial expression recognition based on SVM",
abstract = "Using Kinect acquired RGB-D image to obtain a face feature parameters and three-dimensional coordinates of the characteristic parameters, and to select the characteristic parameter Facial by Candide-3 model, and feature extraction and normalization. Smile face expression data collection through Kinect, SVM collected to smiley face data classify and output the result of recognition, and the results compared with two-dimensional image of smiling face expression recognition results. Experimental results show that three-dimensional image of smiling face expression recognition accuracy than the two-dimensional image of smiling face. This research has important significance for the research and application of facial expression recognition technology.",
keywords = "Facial Expression Recognition, Feature Extraction, Kinect, Support Vector Machine",
author = "Shuming Liu and Xiaopeng Chen and Di Fan and Xu Chen and Fei Meng and Qiang Huang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 13th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016 ; Conference date: 07-08-2016 Through 10-08-2016",
year = "2016",
month = sep,
day = "1",
doi = "10.1109/ICMA.2016.7558813",
language = "English",
series = "2016 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1661--1666",
booktitle = "2016 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016",
address = "United States",
}
