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Summary

This study introduces an improved classroom emotion recognition algorithm using a multi-task convolutional neural network (MTCNN) to accurately identify student emotions, even with obscured faces. The developed system achieves high accuracy, enhancing educational quality through better emotional understanding.

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Area of Science:

Computer Science
Artificial Intelligence
Educational Technology

Background:

Improving classroom teaching quality requires effective methods for understanding student emotional states.
Facial emotion recognition is challenging due to occlusions and subtle expressions in educational settings.

Purpose of the Study:

To develop a robust classroom emotion recognition algorithm for enhanced teaching quality.
To improve the accuracy of facial emotion recognition, particularly under conditions of facial obscuration.

Main Methods:

Utilized a multi-task convolutional neural network (MTCNN) for face detection and image segmentation.
Employed an attention mechanism network and a designed loss function to focus on salient, unobscured facial features.
Developed a visual motion analysis method fusing overall and local image features.

Main Results:

The proposed MTCNN model achieved a recognition accuracy of 93.53% on the classroom dataset.
Compared to VGG16 (75.03%) and ResNet18 (78.26%), MTCNN demonstrated superior performance.
The attention mechanism and designed loss function effectively improved recognition accuracy under obscuration.

Conclusions:

The developed MTCNN-based algorithm significantly enhances classroom emotion recognition accuracy.
The method is effective in identifying student emotions, even with partial facial occlusions.
This technology holds potential for improving pedagogical strategies and student engagement.