An Automated Facial Expression Recognition Program

This is a highly practical source code for automated facial expression recognition. The system implements a multi-stage processing pipeline: first employing the Viola-Jones algorithm for robust face detection through cascade classifiers, then extracting facial features using Gabor filters that capture texture patterns at multiple orientations and scales. These extracted features serve as input to a trained neural network that classifies expressions into categories such as happiness, sadness, or surprise. This codebase not only plays a significant role in facial recognition technologies but also finds applications across diverse domains including emotion analysis, user experience research, and human-computer interaction systems. By utilizing this implementation, researchers can gain deeper insights into the characteristics and significance of human facial expressions, thereby enhancing our understanding of emotional communication and interpersonal dynamics.