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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

A Facial Expression Recognition Method Based on Improved VGG19 Model

Author 1: Lihua Bi Author 2: Shenbo Tang Author 3: Canlin Li
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 15, No. 7 · Published 2024

DOI: https://doi.org/10.14569/IJACSA.2024.0150725

Abstract

With the increasing demand for human-computer interaction and the development of emotional computing technology, facial expression recognition has become a major focus in research. In this paper, an improved VGG19 network model is proposed by involving enhancement strategies, and the facial expression recognition process with the improved VGG19 model is provided. We validated the model on FER2013 and CK+ datasets and conducted comparative experiments on facial expression recognition accuracy among the improved VGG19 and other classic models, including the original VGG19. Instance tests were also performed, using probability histograms to reflect the effectiveness of expression recognition. These experiments and tests demonstrate the superiority, as well as the applicability and stability of the improved VGG19 model on facial expression recognition.

Keywords

How to Cite this Article

Bi, L., Tang, S., & Li, C. (2024). A Facial Expression Recognition Method Based on Improved VGG19 Model. International Journal of Advanced Computer Science and Applications, 15(7). https://doi.org/10.14569/IJACSA.2024.0150725

Bi, Lihua, et al.. "A Facial Expression Recognition Method Based on Improved VGG19 Model." International Journal of Advanced Computer Science and Applications, vol. 15, no. 7, 2024, https://doi.org/10.14569/IJACSA.2024.0150725.

@article{Bi2024,
  title     = {A Facial Expression Recognition Method Based on Improved VGG19 Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Lihua Bi and Shenbo Tang and Canlin Li},
  doi       = {10.14569/IJACSA.2024.0150725},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150725}
}

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