Human emotion recognition with convolutional neural network

Research Article
Open access

Human emotion recognition with convolutional neural network

Yu Zhang 1*
  • 1 Computer Science, Tianjin Ren'ai College, Tianjin, China.    
  • *corresponding author 2019302060185@cuc.edu.cn
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

The interaction between intelligent robots and humans has always been a hot issue, and researchers hope to make human-robot interaction as harmonious as human-human interaction. To achieve this, it is particularly important to enable robots to recognize human facial emotions automatically. However, many intelligent robots can already understand people's emotions through vocal communication. However, some people do not like to express their feelings through words, so it would be more convenient to let machines can automatically analyze people's facial emotions. This paper aims to make the machine recognize people's facial expressions and automatically analyze their emotions to make human-computer interaction more harmonious. The convolutional neural network has shown great influence on image feature extraction in the development of the machine learning field today. Therefore, this paper will adopt the advanced method of CNN to train the model on the FER2013 dataset. The abundant experiments demonstrate that the final trained model has good accuracy in recognizing three emotions: happy, surprise, and neutral.

Keywords:

Convolutional neural network, facial expression recognition.

Zhang,Y. (2023). Human emotion recognition with convolutional neural network . Applied and Computational Engineering,5,81-86.
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References

[1]. Ekman P and Friesen WV 1978 Facial action coding system Environmental Psychology & Nonverbal Behavior

[2]. Ekman P 1993 Facial expression and emotion American psychologist 48(4) 384-92

[3]. De A, Saha A and Pal MC 2015 A human facial expression recognition model based on eigen face Procedia Computer Science 45 282-9

[4]. Owusu E, Zhan Y and Mao QR 2014 An SVM-AdaBoost facial expression recognition system Applied Intelligence 40(3) 536–45

[5]. Lecun Y, Bottou L, Bengio Y and Haffner P 1998 Gradient-based learning applied to document recognition Proceedings of the IEEE 86(11) 2278–324

[6]. Ravi R and Yadhukrishna SV 2020 A face expression recognition using CNN & LBP Fourth International Conference on Computing Methodologies and Communication (ICCMC) 684-9

[7]. Xie W, Jia X, Shen L and Yang M 2019 Sparse deep feature learning for facial expression recognition Pattern Recognition 96 106966

[8]. Yamashita R, Nishio M, Do RKG and Togashi K 2018 Convolutional neural networks: an overview and application in radiology Insights into Imaging 9(4) 611–29

[9]. Zafar A, Aamir M, Mohd Nawi N, Arshad A, Riaz S, Alruban A, Dutta AK and Almotairi S 2022 A comparison of pooling methods for convolutional neural networks Applied Sciences 12(17) 8643

[10]. Ramachandran P, Zoph B and Le QV 2017 Searching for activation functions arXiv preprint arXiv:1710.05941

[11]. Goodfellow IJ, et al. 2013 Challenges in representation learning: a report on three machine learning contests International conference on neural information processing 117–24

[12]. Simonyan K and Zisserman A 2014 Very deep convolutional networks for large-scale image recognition arXiv preprint arXiv:1409.1556

Cite this article

Zhang,Y. (2023). Human emotion recognition with convolutional neural network . Applied and Computational Engineering,5,81-86.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Ekman P and Friesen WV 1978 Facial action coding system Environmental Psychology & Nonverbal Behavior

[2]. Ekman P 1993 Facial expression and emotion American psychologist 48(4) 384-92

[3]. De A, Saha A and Pal MC 2015 A human facial expression recognition model based on eigen face Procedia Computer Science 45 282-9

[4]. Owusu E, Zhan Y and Mao QR 2014 An SVM-AdaBoost facial expression recognition system Applied Intelligence 40(3) 536–45

[5]. Lecun Y, Bottou L, Bengio Y and Haffner P 1998 Gradient-based learning applied to document recognition Proceedings of the IEEE 86(11) 2278–324

[6]. Ravi R and Yadhukrishna SV 2020 A face expression recognition using CNN & LBP Fourth International Conference on Computing Methodologies and Communication (ICCMC) 684-9

[7]. Xie W, Jia X, Shen L and Yang M 2019 Sparse deep feature learning for facial expression recognition Pattern Recognition 96 106966

[8]. Yamashita R, Nishio M, Do RKG and Togashi K 2018 Convolutional neural networks: an overview and application in radiology Insights into Imaging 9(4) 611–29

[9]. Zafar A, Aamir M, Mohd Nawi N, Arshad A, Riaz S, Alruban A, Dutta AK and Almotairi S 2022 A comparison of pooling methods for convolutional neural networks Applied Sciences 12(17) 8643

[10]. Ramachandran P, Zoph B and Le QV 2017 Searching for activation functions arXiv preprint arXiv:1710.05941

[11]. Goodfellow IJ, et al. 2013 Challenges in representation learning: a report on three machine learning contests International conference on neural information processing 117–24

[12]. Simonyan K and Zisserman A 2014 Very deep convolutional networks for large-scale image recognition arXiv preprint arXiv:1409.1556

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