Facial emotion recognition based on auxiliary classifiers and SENet module

Yujie Shang; Fei Yan; Yunqing Liu; Qi Li

doi:10.1117/12.3033244

29 July 2024 Facial emotion recognition based on auxiliary classifiers and SENet module

Yujie Shang, Fei Yan, Yunqing Liu, Qi Li

Proceedings Volume 13214, Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024); 1321404 (2024) https://doi.org/10.1117/12.3033244
Event: Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024), 2024, Guangzhou, China

Abstract

When using convolutional neural networks (CNNs) for facial emotion recognition, down-sampling in the convolutional layers often leads to the loss of many features. These lost features also play a role in emotion recognition and can affect the effectiveness of emotion recognition. Therefore, we propose a facial emotion recognition method based on auxiliary classifiers and SENet modules to effectively alleviate the problem of feature loss during the convolution process. By using SENet modules, the model enhances its focus on different channel features during the feature extraction process, enabling the model to pay better attention to important features. Subsequently, auxiliary classifiers are introduced into the model to effectively utilize features extracted from different layers for auxiliary classification. Finally, the auxiliary classification results are adaptively weighted and fused with the main classifier's classification results. Experimental results demonstrate that introducing auxiliary classifiers and SENet modules further improves the model's accuracy, reaching 68.87%.

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

Citation Download Citation

Yujie Shang, Fei Yan, Yunqing Liu, and Qi Li "Facial emotion recognition based on auxiliary classifiers and SENet module", Proc. SPIE 13214, Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024), 1321404 (29 July 2024); https://doi.org/10.1117/12.3033244

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KEYWORDS

Emotion

Facial recognition systems

Feature extraction

Data modeling

Education and training

Feature fusion

Machine learning

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