Human facial expression recognition technology based on SGE attention and feature fusion improved VGG16 network

Zhiliang Xiao; Nan Yang; Ying Chen

doi:10.1117/12.3031109

19 July 2024 Human facial expression recognition technology based on SGE attention and feature fusion improved VGG16 network

Zhiliang Xiao, Nan Yang, Ying Chen

Author Affiliations +

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131810Q (2024) https://doi.org/10.1117/12.3031109
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

For improving the insufficient feature extraction and recognition capability of the traditional VGG16 network in the human facial expression recognition task, the study proposes a VGG16 network recognition model based on an SGE (Spatial Group Enhance) attention and feature fusion improvement, which firstly extracts the last three Block extracted from the VGG16 network and then introduces the SGE attention module to enhance the attention mechanism by spatial grouping. features are fused, and then the SGE attention module is introduced to enhance the attention mechanism by spatial grouping. Finally, the recognition effect of the improved model is analyzed, and the results show that the improved VGG16 network model improves the human facial expression recognition accuracy by 6.9%, 7.8%, and 7.4% on Cohn-Kanade, Multi-Media Interface, and AffectNet datasets, respectively, and the recognition accuracy is about 90% or more for eight common human facial expressions. The recognition accuracy for eight common human facial expressions is about 90% or more. The above data show that the research model can recognize human facial expressions more accurately.

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

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Zhiliang Xiao, Nan Yang, and Ying Chen "Human facial expression recognition technology based on SGE attention and feature fusion improved VGG16 network", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131810Q (19 July 2024); https://doi.org/10.1117/12.3031109

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KEYWORDS

Facial recognition systems

Data modeling

Feature fusion

Feature extraction

Image processing

Image classification

Visual process modeling

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