MATEC Web Conf.
Volume 173, 20182018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
|Number of page(s)||5|
|Section||Digital Signal and Image Processing|
|Published online||19 June 2018|
An expression recognition algorithm based on convolution neural network and RGB-D Images
National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, China
2 Information Construction Office Hubei University of Chinese Medicine, Wuhan, 430065, China
* Corresponding author: firstname.lastname@example.org
Aiming at the problem of recognition effect is not stable when 2D facial expression recognition in the complex illumination and posture changes. A facial expression recognition algorithm based on RGB-D dynamic sequence analysis is proposed. The algorithm uses LBP features which are robust to illumination, and adds depth information to study the facial expression recognition. The algorithm firstly extracts 3D texture features of preprocessed RGB-D facial expression sequence, and then uses the CNN to train the dataset. At the same time, in order to verify the performance of the algorithm, a comprehensive facial expression library including 2D image, video and 3D depth information is constructed with the help of Intel RealSense technology. The experimental results show that the proposed algorithm has some advantages over other RGB-D facial expression recognition algorithms in training time and recognition rate, and has certain reference value for future research in facial expression recognition.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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