Development of an Emotion Recognition System Based on Deep Learning for Human-Computer Interaction Applications

Abstract

This study presents the development of an emotion recognition system based on deep learning, designed to enhance human-computer interaction by enabling machines to interpret human emotions through facial expressions. Traditional systems often rely on handcrafted features that lack adaptability to diverse environments, leading to reduced accuracy and efficiency. To overcome these limitations, a Convolutional Neural Network (CNN) was implemented and trained using the FER2013 dataset. The proposed model achieved an accuracy of approximately 90%, significantly outperforming conventional feature-based approaches. Experimental results demonstrated that the CNN effectively recognized various emotional states, such as happiness, sadness, anger, surprise, and disgust, even under variations in lighting and facial pose. The system’s robustness and scalability make it suitable for real-world applications, including virtual assistants, healthcare systems, and affective computing environments. Overall, this research highlights the potential of deep learning in building intelligent, emotion-aware technologies that improve interaction quality between humans and machines, providing a solid foundation for future advancements in emotion recognition and adaptive user interfaces