Facial expression recognition based on Feature Pyramid Network

Research Article
Open access

Facial expression recognition based on Feature Pyramid Network

Yongcheng Huang 1*
  • 1 Delft University of Technology    
  • *corresponding author Y.Huang-51@student.tudelft.nl
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/21/20231109
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

Facial expression recognition with significant implications across fields such as psychology, computer science, and artificial intelligence. This paper proposes a combination of a Feature Pyramid Network (FPN) and a Residual Network (ResNet) to construct a recognition model. The main objective of the proposed model is to refine the multi-level feature representation of facial expressions. This approach aims to provide a more holistic understanding of the diverse and complex nature of facial expressions, recognizing the intricate interplay between macro and micro-expressions. Experimental results underscore the model's considerable superiority over traditional methods, particularly in terms of accuracy and adaptability to objects of varying sizes and complexities. This comprehensive approach to facial expression recognition showcases the potential of integrating different neural network architectures, furthering our understanding of the subtleties of facial expressions. The research, therefore, presents a significant contribution to the field of facial expression recognition, demonstrating the efficacy of integrating multi-scale feature extraction techniques to improve model performance. It sets the stage for future research directions in this domain, paving the way for more sophisticated emotion recognition systems that can be deployed in real-world applications.

Keywords:

facial expression recognition, Feature Pyramid Network, Residual Network

Huang,Y. (2023). Facial expression recognition based on Feature Pyramid Network. Applied and Computational Engineering,21,20-27.
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References

[1]. Bettadapura V 2012 Face expression recognition and analysis: the state of the art arXiv preprint arXiv:1203.6722

[2]. Happy S Routray A 2014 Automatic facial expression recognition using features of salient facial patches IEEE transactions on Affective Computing 6(1): pp 1-12

[3]. De S Liyanage C Suen C 2003 Real-time facial feature extraction and emotion recognition Fourth international conference on information, communications and signal processing IEEE pp 1310-1314

[4]. Balasubramanian B 2019 Analysis of facial emotion recognition 3rd International Conference on Trends in Electronics and Informatics (ICOEI) IEEE 2019 pp 945-949

[5]. Kartali A 2018 Real-time algorithms for facial emotion recognition: A comparison of different approaches 14th Symposium on Neural Networks and Applications (NEUREL) IEEE pp 1-4

[6]. Bargal S Barsoum E Ferrer C 2016 Emotion recognition in the wild from videos using images 18th ACM International Conference on Multimodal Interaction ACM pp 433-436

[7]. Lin T 2017 Feature pyramid networks for object detection IEEE conference on computer vision and pattern recognition IEEE pp 2117-2125

[8]. Li B Lima D 2021 Facial expression recognition via ResNet-50 International Journal of Cognitive Computing in Engineering 2: pp 57-64

[9]. Zhao G Ge W Yu Y 2021 GraphFPN: Graph feature pyramid network for object detection IEEE international conference on computer vision (CVPR) IEEE pp 2763-2772

[10]. Giannopoulos P Perikos I Hatzilygeroudis I 2018 Deep learning approaches for facial emotion recognition: A case study on FER-2013 Advances in Hybridization of Intelligent Methods: Models, Systems and Applications pp 1-16

[11]. Goodfellow I Erhan D Carrier P et al 2013 Challenges in representation learning: A report on three machine learning contests Neural Information Processing: 20th International Conference (ICONIP) Springer berlin Heidelberg pp 117-124

Cite this article

Huang,Y. (2023). Facial expression recognition based on Feature Pyramid Network. Applied and Computational Engineering,21,20-27.

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]. Bettadapura V 2012 Face expression recognition and analysis: the state of the art arXiv preprint arXiv:1203.6722

[2]. Happy S Routray A 2014 Automatic facial expression recognition using features of salient facial patches IEEE transactions on Affective Computing 6(1): pp 1-12

[3]. De S Liyanage C Suen C 2003 Real-time facial feature extraction and emotion recognition Fourth international conference on information, communications and signal processing IEEE pp 1310-1314

[4]. Balasubramanian B 2019 Analysis of facial emotion recognition 3rd International Conference on Trends in Electronics and Informatics (ICOEI) IEEE 2019 pp 945-949

[5]. Kartali A 2018 Real-time algorithms for facial emotion recognition: A comparison of different approaches 14th Symposium on Neural Networks and Applications (NEUREL) IEEE pp 1-4

[6]. Bargal S Barsoum E Ferrer C 2016 Emotion recognition in the wild from videos using images 18th ACM International Conference on Multimodal Interaction ACM pp 433-436

[7]. Lin T 2017 Feature pyramid networks for object detection IEEE conference on computer vision and pattern recognition IEEE pp 2117-2125

[8]. Li B Lima D 2021 Facial expression recognition via ResNet-50 International Journal of Cognitive Computing in Engineering 2: pp 57-64

[9]. Zhao G Ge W Yu Y 2021 GraphFPN: Graph feature pyramid network for object detection IEEE international conference on computer vision (CVPR) IEEE pp 2763-2772

[10]. Giannopoulos P Perikos I Hatzilygeroudis I 2018 Deep learning approaches for facial emotion recognition: A case study on FER-2013 Advances in Hybridization of Intelligent Methods: Models, Systems and Applications pp 1-16

[11]. Goodfellow I Erhan D Carrier P et al 2013 Challenges in representation learning: A report on three machine learning contests Neural Information Processing: 20th International Conference (ICONIP) Springer berlin Heidelberg pp 117-124

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