TY - GEN
T1 - Real-Time facial expression recognition based on cnn
AU - Liu, Keng Cheng
AU - Hsu, Chen Chien
AU - Wang, Wei Yen
AU - Chiang, Hsin Han
N1 - Funding Information:
This work was financially supported by the “Chinese Language and Technology Center” of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, and Ministry of Science and Technology, Taiwan, under Grants no. MOST 108-2634-F-003-002 and MOST 108-2634-F-003-003 through Pervasive Artificial Intelligence Research (PAIR) Labs. We are grateful to the National Center for High-performance Computing for computer time and facilities to conduct this research.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - In this paper, we propose a method for improving the robustness of real-Time facial expression recognition. Although there are many ways to improve the accuracy of facial expression recognition, a revamp of the training framework and image preprocessing allow better results in applications. One existing problem is that when the camera is capturing images in high speed, changes in image characteristics may occur at certain moments due to the influence of light and other factors. Such changes can result in incorrect recognition of the human facial expression. To solve this problem for smooth system operation and maintenance of recognition speed, we take changes in image characteristics at high speed capturing into account. The proposed method does not use the immediate output for reference, but refers to the previous image for averaging to facilitate recognition. In this way, we are able to reduce interference by the characteristics of the images. The experimental results show that after adopting this method, overall robustness and accuracy of facial expression recognition have been greatly improved compared to those obtained by only the convolution neural network (CNN).
AB - In this paper, we propose a method for improving the robustness of real-Time facial expression recognition. Although there are many ways to improve the accuracy of facial expression recognition, a revamp of the training framework and image preprocessing allow better results in applications. One existing problem is that when the camera is capturing images in high speed, changes in image characteristics may occur at certain moments due to the influence of light and other factors. Such changes can result in incorrect recognition of the human facial expression. To solve this problem for smooth system operation and maintenance of recognition speed, we take changes in image characteristics at high speed capturing into account. The proposed method does not use the immediate output for reference, but refers to the previous image for averaging to facilitate recognition. In this way, we are able to reduce interference by the characteristics of the images. The experimental results show that after adopting this method, overall robustness and accuracy of facial expression recognition have been greatly improved compared to those obtained by only the convolution neural network (CNN).
KW - average weighting method
KW - convolution neural network (CNN)
KW - facial expression recognition
UR - http://www.scopus.com/inward/record.url?scp=85072919800&partnerID=8YFLogxK
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U2 - 10.1109/ICSSE.2019.8823409
DO - 10.1109/ICSSE.2019.8823409
M3 - Conference contribution
AN - SCOPUS:85072919800
T3 - Proceedings of 2019 International Conference on System Science and Engineering, ICSSE 2019
SP - 120
EP - 123
BT - Proceedings of 2019 International Conference on System Science and Engineering, ICSSE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on System Science and Engineering, ICSSE 2019
Y2 - 20 July 2019 through 21 July 2019
ER -