The research of facial expression image recognition method based on MobileNetV3

Xinyue Zou; Chenguang Liu; Xuebin Xu; Rong Zhang

doi:10.1117/12.3029622

21 June 2024 The research of facial expression image recognition method based on MobileNetV3

Xinyue Zou, Chenguang Liu, Xuebin Xu, Rong Zhang

Author Affiliations +

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131673G (2024) https://doi.org/10.1117/12.3029622
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

The information conveyed through facial expressions accounts for a large proportion of the total information and can effectively express people's intentions and emotions. Facial expression recognition has laid the foundation for fields such as human-computer interaction, facial emotion prediction, and artificial intelligence, and has become an important research object in computer vision. This article proposes a facial expression recognition method based on the MobileNetV3 network for face images from different angles. The method uses depth-wise separable convolution, introduces attention mechanism and new activation function to update blocks, and redesigns the time-consuming layer structure at the end. The dataset used in this article is the KDEF, which includes 4,900 color images with a size of 562*762 pixels. Through extensive experiments, it has been shown that the proposed structure in this article improves the accuracy of facial expression recognition from different angles compared to other network structures, achieving 94.7%, and has a smaller parameter count, which is beneficial for further research on facial expressions.

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

Citation Download Citation

Xinyue Zou, Chenguang Liu, Xuebin Xu, and Rong Zhang "The research of facial expression image recognition method based on MobileNetV3", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131673G (21 June 2024); https://doi.org/10.1117/12.3029622

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