Evaluation of a CNN-Based Model for Predicting Human Psychological States Using Facial Features: Benefits and Limitations

This research evaluates the advantages and limitations of a CNN-based model for predicting human psychological states through facial analysis. This approach innovatively leverages CNNs to interpret facial expressions, which are essential for mental health assessment and human-computer interaction. The system provides an innovative approach to understanding human emotions by analysing facial expressions, a critical aspect of mental health assessment and human-computer interaction. However, while the method offers substantial advancements in emotion recognition, it faces challenges such as computational costs, dataset limitations, and an over-reliance on facial features. This paper explores the model's strengths and limitations, proposing improvements and suggesting directions for future research.