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Research on Facial Expression Recognition Technology Based on Convolutional-Neural-Network Structure

Research on Facial Expression Recognition Technology Based on Convolutional-Neural-Network Structure

Junqi Guo, Ke Shan, Hao Wu, Rongfang Bie, Wenwan You, Di Lu
Copyright: © 2018 |Volume: 6 |Issue: 4 |Pages: 14
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781522546863|DOI: 10.4018/IJSI.2018100108
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MLA

Guo, Junqi, et al. "Research on Facial Expression Recognition Technology Based on Convolutional-Neural-Network Structure." IJSI vol.6, no.4 2018: pp.103-116. http://doi.org/10.4018/IJSI.2018100108

APA

Guo, J., Shan, K., Wu, H., Bie, R., You, W., & Lu, D. (2018). Research on Facial Expression Recognition Technology Based on Convolutional-Neural-Network Structure. International Journal of Software Innovation (IJSI), 6(4), 103-116. http://doi.org/10.4018/IJSI.2018100108

Chicago

Guo, Junqi, et al. "Research on Facial Expression Recognition Technology Based on Convolutional-Neural-Network Structure," International Journal of Software Innovation (IJSI) 6, no.4: 103-116. http://doi.org/10.4018/IJSI.2018100108

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Abstract

Human facial expressions change so subtly that recognition accuracy of most traditional approaches largely depend on feature extraction. In this article, the authors employ a deep convolutional neural network (CNN) to devise a facial expression recognition system to discover deeper feature representation of facial expression. The proposed system is composed of the input module, the pre-processing module, the recognition module and the output module. The authors introduce jaffe and ck+ to simulate and evaluate the performance under the influence of different factors (e.g. network structure, learning rate and pre-processing). The authors also examine the anti-noise property of the system with zero-mean gaussian white noise. In addition, they simulate the recognition accuracy on different expression pairs and discuss the confusion issue on similar expression recognition. Finally, they introduce the k-nearest neighbor (KNN) algorithm compared with CNN to make the results more convincing.

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