Optimization of Emotion Recognition System using Facial Expressions

Abstract

Emotion recognition using facial expressions has gained significant attention in the field of computer vision and artificial intelligence due to its wide range of applications in human-computer interaction, security, healthcare, and e-learning systems. This project focuses on optimizing an emotion recognition system by leveraging facial expression analysis to enhance recognition accuracy, robustness, and computational efficiency. The proposed system utilizes deep learning techniques, particularly Convolutional Neural Networks (CNNs), for automatic feature extraction and classification of emotions such as happiness, sadness, anger, fear, surprise, disgust, and neutrality. To improve performance, several optimization strategies are implemented including data augmentation, facial landmark detection, transfer learning with pretrained models, and hyperparameter tuning. Additionally, lightweight architectures are explored to ensure real-time performance on low-power devices. The system is trained and evaluated on benchmark datasets such as FER-2013 and CK+, achieving promising results in recognizing emotions under varied conditions. This work contributes to the development of an intelligent, real-time emotion recognition system that can be integrated into various practical applications requiring emotional context awareness.