Classroom facial expression recognition based on improved YOLOv4

Yinan Liu; Jia Wang; Yang Li; Shaozhong Cao

doi:10.1117/12.3029714

21 June 2024 Classroom facial expression recognition based on improved YOLOv4

Yinan Liu, Jia Wang, Yang Li, Shaozhong Cao

Author Affiliations +

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131673D (2024) https://doi.org/10.1117/12.3029714
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

In response to the current challenges of poor real-time performance and low accuracy in facial expression recognition in classroom environments, this paper proposes a classroom facial recognition model based on YOLOv4. The original backbone feature extraction network is replaced with DenseNet121, and the SE attention mechanism is incorporated into the model to enhance feature extraction and representation capabilities. The EIoU is used for localization loss calculation to improve the localization accuracy of small targets. The improved model enables real-time prediction of multiple facial expressions. Validation on a self-built dataset demonstrates that the improved model achieves a 16.33% increase in mAP compared to other models, with an AP of 96.51% for commonly occurring facial expressions in the classroom. The model achieves a video inference speed of up to 25fps and maintains good recognition performance in online teaching scenarios, indicating a certain level of generalization.

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

Citation Download Citation

Yinan Liu, Jia Wang, Yang Li, and Shaozhong Cao "Classroom facial expression recognition based on improved YOLOv4", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131673D (21 June 2024); https://doi.org/10.1117/12.3029714

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