Time Facial Expression Recognition Using Optimized CNN Models for Behavioral and Emotional Analysis

Facial expression recognition is a significant field in human-computer interaction, aiming to analyze emotions such as happiness, sadness, anger, and fear. This study develops a facial expression detection system using Convolutional Neural Networks (CNN) to address challenges like lighting variations and facial angles. The research begins with collecting and preprocessing datasets, including FER-2013, to normalize, augment, and label images for seven emotion classes. The CNN model is designed with convolutional, pooling, and fully connected layers, optimized using ReLU activation, Adam optimizer, and categorical crossentropy loss function. Training is conducted on 80% of the dataset, with 20% for validation, achieving a validation accuracy of 91.7%. System performance is evaluated using precision, recall, F1-score, and real-time testing integrated with cameras, achieving an average detection accuracy of 90%. Results demonstrate the system's robustness in detecting emotions under varying conditions, highlighting its potential for applications in security, education, and emotional therapy. Future research recommends incorporating larger datasets and advanced transfer learning methods to improve system efficiency and accuracy.