Determining emotions from students' facial expressions using CNN

Abstract

Recognizing and understanding human emotions, particularly in educational settings, is of great importance. This research focuses on utilizing Convolutional Neural Networks (CNNs) to accurately identify students' emotions based on their facial expressions. By leveraging facial cues, an automated system can be developed to effectively recognize and interpret emotions in educational contexts. A diverse dataset of facial images featuring students expressing various emotions is carefully curated for this study. Facial landmarks and action units are extracted to capture essential information from different facial regions. These images are meticulously annotated with ground truth labels, ensuring precise training and evaluation of the CNN model. CNNs are chosen as the core technology for feature extraction and emotion classification due to their ability to learn intricate spatial patterns and hierarchical representations. Extensive training, including techniques like data augmentation and transfer learning, enables the model to generalize and adapt to a wide range of emotional expressions. The performance of the CNN model is evaluated using metrics such as accuracy, precision, recall, and F1 score. Thorough experiments compare the proposed CNN approach with existing methods for facial emotion recognition, demonstrating the superior performance of the CNN model in accurately identifying students' emotions from facial expressions.