Comparison on machine learning based algorithms for face expression recognition

Research Article
Open access

Comparison on machine learning based algorithms for face expression recognition

Jiayi Bai 1* , Jiawei He 2 , Fengyuan Liu 3
  • 1 Legacy Christian Academy, 334 Giotto, United States    
  • 2 Legacy Christian Academy, 2813 Martel Dr. Dayton OH, United State    
  • 3 The Shiyan school Attached To Shijazhuang No.2 Middle School, Shijia Zhuang, Hebei, China    
  • *corresponding author 23baijiayi@iusd.org
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

With the development of machine learning methods, most of tasks has benefited from it. Numerous works have been done to improve the accuracy and robustness of these methods. In this paper, the authors aim to exploit the effectiveness of machine learning algorithms in face expression recognition (FER) task. Three methods, including support vector machine, random forest and convolutional neural network are introduced to test the performance in FER task. Three methods are tested in the public dataset, FER2013. The conclusion is drawn as follow. All these methods can handle the image recognition, which is a 7-class classification task. Furthermore, nerual network achieves the best performance, which achieves 65% accuracy. The paper exploit the potential of machine learning methods and give a brief attempt on applying the popular algorithms into real-world image recognition tasks.

Keywords:

Face expression recognition, support vector machine, neural network, random forest.

Bai,J.;He,J.;Liu,F. (2023). Comparison on machine learning based algorithms for face expression recognition. Applied and Computational Engineering,5,833-836.
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References

[1]. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

[2]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.

[3]. Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., ... & Iyengar, S. S. (2018). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys (CSUR), 51(5), 1-36.

[4]. Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE Access, 7, 53040-53065.

[5]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.

[6]. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[7]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

[8]. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).

[9]. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).

[10]. Goodfellow, I. J., Erhan, D., Carrier, P. L., Courville, A., Mirza, M., Hamner, B., ... & Bengio, Y. (2013). Challenges in representation learning: A report on three machine learning contests. In International conference on neural information processing (pp. 117-124).

Cite this article

Bai,J.;He,J.;Liu,F. (2023). Comparison on machine learning based algorithms for face expression recognition. Applied and Computational Engineering,5,833-836.

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]. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

[2]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.

[3]. Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., ... & Iyengar, S. S. (2018). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys (CSUR), 51(5), 1-36.

[4]. Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE Access, 7, 53040-53065.

[5]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.

[6]. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[7]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

[8]. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).

[9]. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).

[10]. Goodfellow, I. J., Erhan, D., Carrier, P. L., Courville, A., Mirza, M., Hamner, B., ... & Bengio, Y. (2013). Challenges in representation learning: A report on three machine learning contests. In International conference on neural information processing (pp. 117-124).

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