Research on Classroom Emotion Recognition Algorithm Based on Visual Emotion Classification

• Published in: Computational intelligence and neuroscience 2022-08, Vol.2022, p.1-10
• Main Author: Yuan, Qinying
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Artificial neural networks; Classification; Classrooms; Criteria; Datasets; Education; Emotion recognition; Emotions; Face recognition; Image processing; Image segmentation; Learning; Methods; Model accuracy; Motion detection; Neural networks; Occultation; Pattern recognition; Sentiment analysis; Social networks; Students; Teaching; Training
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/6453499

In this paper, we construct a classroom emotion recognition algorithm by classifying visual emotions for improving the quality of classroom teaching. We assign weights to the training images through an attention mechanism network and then add a designed loss function so that it can focus on the feature parts of face images that are not obscured and can characterize the target emotion, thus improving the accuracy of facial emotion recognition under obscuration. Analyze the salient expression features of classroom students and establish a classification criteria and criteria library. The videos of classroom students’ facial expressions are collected, a multi-task convolutional neural network (MTCNN) is used for face detection and image segmentation, and the ones with better feature morphology are selected to build a standard database. A visual motion analysis method with the fusion of overall and local features of the image is proposed. To validate the effectiveness of the designed MTCNN model, two mainstream classification networks, VGG16 and ResNet18, were tested and compared with MTCNN by training on RAF-DB, masked dataset, and the classroom dataset constructed in this paper, and the final accuracy after training was 78.26% and 75.03% for ResNet18 and VGG16, respectively. The results show that the MTCNN proposed in this paper has a better recognition effect. The test results of the loss function also show that it can effectively improve the recognition accuracy, and the MTCNN model has an accuracy of 93.53% for recognizing students’ facial emotions. Finally, the dataset is extended with the training method of expression features, and the experimental study shows that the method performs well and can carry out recognition effectively.