Facial Emotion Recognition using Deep Learning

Abstract

Facial Emotion Recognition (FER) is crucial in human and computer interaction, emotion analysis, and behavioral research. This study seeks to create a reliable FER system by integrating different methods from computer vision and pattern recognition. The suggested method breaks down the FER process into 3 components: Preprocessing, Feature Extraction, and Classification. Each component is examined with several potential methods, and their effectiveness is evaluated to identify the best combination. By utilizing cutting-edge algorithms in deep learning and image processing, the system improves the reliability as well as the accuracy of Facial Emotion detection. The experimental findings emphasize the efficacy of the chosen methods in reaching high recognition rates across a range of datasets. These findings advance affective computing and have practical applications in areas like sentiment analysis, security, and healthcare monitoring.