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It is challenging for teachers to monitor each student's emotional state in real-time, making personalized learning difficult to achieve. Previous emotion recognition methods, such as support vector machines, are limited by technology and fail to meet practical application requirements. However, the development of deep learning technology offers new solutions for facial expression recognition, which makes emotional interaction and personalized support in education possible. Until now, there has been a lack of facial expression datasets in real classroom settings. To fill this gap, this study collected facial expression data in a real classroom, preprocessed it using OpenCV, and established the first real-world facial expression dataset. The emotion categories include surprise, happiness, neutrality, confusion, and boredom. The dataset was rigorously screened and contains a total of 5,527 images, divided into training, validation, and test sets. This dataset provides a reliable foundation for future research and applications in educational technology, particularly in the development of real-time emotion recognition models to enhance personalized learning and teaching effectiveness. The rigorous data collection and preprocessing approach ensures the dataset's quality and authenticity, addressing the limitations of existing datasets collected in laboratory settings. |
