Face-emotion classification guided by deep convolutional neural network

Research Article
Open access

Face-emotion classification guided by deep convolutional neural network

Shujie Wu 1*
  • 1 Capital University of Economics and Business    
  • *corresponding author wushujie@cueb.edu.cn
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/21/20231111
ACE Vol.21
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-033-2
ISBN (Online): 978-1-83558-034-9

Abstract

With the rapid development of computer vision and convolutional neural networks, the task of automatic face emotion classification has become a reality. The aim of this study is to improve the underlying neural network model to achieve effective face emotion classification. By presenting a simplified network to generate the recognition model, the author enhances the underlying neural network architecture. The model, in particular, augments the underlying neural network with a convolutional layer, a maximum pooling layer, and a discard layer, and increases the number of neurons in the dense layer from 25 to 128. The convolutional layer allows for the automatic extraction of sentiment features. To decrease the parameters in the feature maps, the maximum pooling layer is applied. The experiments are constructed on the Facial Emotion Recognition 2013 dataset (FER-2013). The streamlined network model improves performance by 6% to 56.32% as compared with the basic neural network model. Numerous experiments show that the proposed streamlined network model can effectively recognize facial emotions. In addition, the author analysis the confusion matrix and finds that the model has weak feedback for aversive emotions. Future research will focus on improving the representation of unclear features such as aversive emotions to enhance model generalization.

Keywords:

face emotion detection, computer vision, convolutional neural networks, confusion matrix

Wu,S. (2023). Face-emotion classification guided by deep convolutional neural network. Applied and Computational Engineering,21,153-160.
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References

[1]. Mehrotra R Namuduri K Ranganathan N 1992 Gabor filter-based edge detection Pattern recognition 25(12): pp 1479-1494

[2]. O'Connor B Roy K 2013 Facial recognition using modified local binary pattern and random forest International Journal of Artificial Intelligence & Applications 4(6): p 25

[3]. Chakraverty S Sahoo D Mahato N Chakraverty S Sahoo D Mahato N 2019 McCulloch–Pitts neural network model Concepts of soft computing: fuzzy and ANN with programming pp 167-173

[4]. LeCun Y Bottou L Bengio Y Haffner P 1998 Gradient-based learning applied to document recognition Proceedings of the IEEE 86(11): pp 2278-2324

[5]. Qassim H Verma A Feinzimer D 2018 January Compressed residual-VGG16 CNN model for big data places image recognition In 2018 IEEE 8th annual computing and communication workshop and conference (CCWC) IEEE pp 169-175

[6]. Liang J 2020 September Image classification based on RESNET In Journal of Physics: Conference Series IOP Publishing 1634(1): p 012110

[7]. Wang M Lu S Zhu D Lin J Wang Z 2018 October A high-speed and low-complexity architecture for softmax function in deep learning 2018 IEEE asia pacific conference on circuits and systems (APCCAS) IEEE pp 223-226

[8]. Gordon-Rodriguez E Loaiza-Ganem G Pleiss G Cunningham J 2020 Uses and abuses of the cross-entropy loss Case studies in modern deep learning

[9]. Mehta S Paunwala C Vaidya B 2019 May CNN based traffic sign classification using adam optimizer 2019 international conference on intelligent computing and control systems (ICCS) IEEE pp 1293-1298

[10]. Visa S Ramsay B Ralescu A Van D 2011 Confusion matrix-based feature selection Maics 710(1): pp 120-127

Cite this article

Wu,S. (2023). Face-emotion classification guided by deep convolutional neural network. Applied and Computational Engineering,21,153-160.

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 5th International Conference on Computing and Data Science

ISBN：978-1-83558-033-2(Print) / 978-1-83558-034-9(Online)
Editor：Roman Bauer, Alan Wang, Marwan Omar
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.21
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Mehrotra R Namuduri K Ranganathan N 1992 Gabor filter-based edge detection Pattern recognition 25(12): pp 1479-1494

[2]. O'Connor B Roy K 2013 Facial recognition using modified local binary pattern and random forest International Journal of Artificial Intelligence & Applications 4(6): p 25

[3]. Chakraverty S Sahoo D Mahato N Chakraverty S Sahoo D Mahato N 2019 McCulloch–Pitts neural network model Concepts of soft computing: fuzzy and ANN with programming pp 167-173

[4]. LeCun Y Bottou L Bengio Y Haffner P 1998 Gradient-based learning applied to document recognition Proceedings of the IEEE 86(11): pp 2278-2324

[5]. Qassim H Verma A Feinzimer D 2018 January Compressed residual-VGG16 CNN model for big data places image recognition In 2018 IEEE 8th annual computing and communication workshop and conference (CCWC) IEEE pp 169-175

[6]. Liang J 2020 September Image classification based on RESNET In Journal of Physics: Conference Series IOP Publishing 1634(1): p 012110

[7]. Wang M Lu S Zhu D Lin J Wang Z 2018 October A high-speed and low-complexity architecture for softmax function in deep learning 2018 IEEE asia pacific conference on circuits and systems (APCCAS) IEEE pp 223-226

[8]. Gordon-Rodriguez E Loaiza-Ganem G Pleiss G Cunningham J 2020 Uses and abuses of the cross-entropy loss Case studies in modern deep learning

[9]. Mehta S Paunwala C Vaidya B 2019 May CNN based traffic sign classification using adam optimizer 2019 international conference on intelligent computing and control systems (ICCS) IEEE pp 1293-1298

[10]. Visa S Ramsay B Ralescu A Van D 2011 Confusion matrix-based feature selection Maics 710(1): pp 120-127

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