Facial expression recognition based on ResNet and transfer learning

Research Article
Open access

Facial expression recognition based on ResNet and transfer learning

Yixuan Chen 1*
  • 1 Xi'an Jiaotong University    
  • *corresponding author zj1561894328@stu.xjtu.edu.cn
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/22/20231168
ACE Vol.22
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-035-6
ISBN (Online): 978-1-83558-036-3

Abstract

With its potential to revolutionize a wide range of applications, including lie detection, social robotics, and driver fatigue detection, facial expression recognition is a field that is rapidly expanding. However, traditional machine learning methods have struggled with facial expression recognition due to limitations such as manual feature selection and limited representation capabilities. Additionally, these methods require large amounts of annotated data, which can be time-consuming and expensive to obtain. In order to overcome these difficulties, this paper suggests a novel method that builds recognition models using a multi-layer perceptron (MLP) and ResNet. This hybrid model offers improved performance over conventional CNN models, achieving an impressive accuracy rate of 85.71% on the FER_2013 dataset. Additionally, migration learning is used to increase the model's precision and avoid over-fitting. The FER_2013 dataset is used to train and test the model. The results of the trials show that the suggested model can recognize facial expressions while minimizing the overfitting problem typically associated with deep learning. The model will eventually include a self-attentive mechanism in the study in an effort to improve model resolution. By using it with color images, the team also hopes to increase the model's capacity for generalization.

Keywords:

Facial expression recognition, ResNet, Deep learning, Transfer learning

Chen,Y. (2023). Facial expression recognition based on ResNet and transfer learning. Applied and Computational Engineering,22,71-78.
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References

[1]. Tian Y Kanade T 2011 Facial Expression Recognition Handbook of Face Recognition Springer London 0487-0519.

[2]. Darwin C Prodger P 1998 The Expression of the Emotions in Man and Anzimals Oxford University Press.

[3]. Tian Y I Kanade T Cohn J F 2001 Recognizing action units for facial expression analysis IEEE Transactions on pattern analysis and machine intelligence 23(2): 0097-0115.

[4]. Li S Deng W 2022 Deep Facial Expression Recognition: A Survey IEEE Transactions on Affective Computing 13(3): 1195-1215.

[5]. Suwa M Sugie N Fujimora K 1978 A preliminary note on pattern recognition of human emotional expression International Joint Conference on Pattern Recognition: 0408–0410.

[6]. Bartlett M S Littlewort G Fasel I Movellan J R 2003 Real time face detection and facial expression recognition: Development and applications to human computer interaction 2003 Conference on computer vision and pattern recognition workshop IEEE 5(1): 0053-0053.

[7]. Moghaddam B Pentland A 1995 Probabilistic visual learning for object detection Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition:0786-0793.

[8]. Lucey P Cohn J F Kanade T Saragih J Ambadar Z Matthews I 2010 The extended Cohn-Kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops:0094-0101.

[9]. 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 Neural Information Processing: 20th International Conference (ICONIP) Springer berlin heidelberg 0117-0124.

[10]. Ge H Zhu Z Dai Y 2022 Facial expression recognition based on deep learning Computer Methods and Programs in Biomedicine 0215-0215.

Cite this article

Chen,Y. (2023). Facial expression recognition based on ResNet and transfer learning. Applied and Computational Engineering,22,71-78.

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：978-1-83558-035-6(Print) / 978-1-83558-036-3(Online)
Editor：Alan Wang, Marwan Omar, Roman Bauer
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.22
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Tian Y Kanade T 2011 Facial Expression Recognition Handbook of Face Recognition Springer London 0487-0519.

[2]. Darwin C Prodger P 1998 The Expression of the Emotions in Man and Anzimals Oxford University Press.

[3]. Tian Y I Kanade T Cohn J F 2001 Recognizing action units for facial expression analysis IEEE Transactions on pattern analysis and machine intelligence 23(2): 0097-0115.

[4]. Li S Deng W 2022 Deep Facial Expression Recognition: A Survey IEEE Transactions on Affective Computing 13(3): 1195-1215.

[5]. Suwa M Sugie N Fujimora K 1978 A preliminary note on pattern recognition of human emotional expression International Joint Conference on Pattern Recognition: 0408–0410.

[6]. Bartlett M S Littlewort G Fasel I Movellan J R 2003 Real time face detection and facial expression recognition: Development and applications to human computer interaction 2003 Conference on computer vision and pattern recognition workshop IEEE 5(1): 0053-0053.

[7]. Moghaddam B Pentland A 1995 Probabilistic visual learning for object detection Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition:0786-0793.

[8]. Lucey P Cohn J F Kanade T Saragih J Ambadar Z Matthews I 2010 The extended Cohn-Kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops:0094-0101.

[9]. 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 Neural Information Processing: 20th International Conference (ICONIP) Springer berlin heidelberg 0117-0124.

[10]. Ge H Zhu Z Dai Y 2022 Facial expression recognition based on deep learning Computer Methods and Programs in Biomedicine 0215-0215.

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