Improved VGG network face expression recognition based on attention mechanism

Linkai He; Xiaomin Pei

doi:10.1117/12.3031322

19 July 2024 Improved VGG network face expression recognition based on attention mechanism

Linkai He, Xiaomin Pei

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 1318107 (2024) https://doi.org/10.1117/12.3031322
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Facial expression recognition is cutting-edge research in the field of computer vision, aiming to deepen the understanding of human emotions. With the popularity of social media and virtual interactions, there is an urgent need to accurately capture facial expressions to improve the user experience. In this paper, we summarize the research status of facial expression recognition, discuss the challenges faced by related technologies, and propose an improved VGG facial expression recognition method based on attention mechanism (AMVGG) to improve the recognition performance and promote the further development of this field. In this paper, it is proposed to embed multiple CBAM modules in the improved VGG network as the backbone network, and introduce deep attention center loss combined with softmax loss function to optimize the network. The experiments on the classical dataset FER2013 and RAF-DB achieved 73.81% and 88.76% accuracy, respectively, which were higher than those of other typical recognition algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Linkai He and Xiaomin Pei "Improved VGG network face expression recognition based on attention mechanism", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 1318107 (19 July 2024); https://doi.org/10.1117/12.3031322

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