A Real-Time Emotion Detection System from Facial Expression Using Convolutional Neural Network

Abstract

Abstract: This paper presents an abstract-real-time facial emotion detection system utilizing convolutional neural networks (CNNs) for robust face recognition, addressing the demand for rapid and accurate emotion classification in dynamic contexts. The proposed CNN-based approach ensures real-time processing while maintaining high accuracy, detailing the architecture of a specialized CNN model tailored for real-time emotion detection. The study optimizes network layers and parameters for efficient facial feature extraction and analysis, substantiated through extensive experiments on diverse datasets, showcasing the system's instant emotion detection proficiency. Rigorous quantitative analysis demonstrates its superior performance compared to existing methods, underscoring its effectiveness. The system's potential to precisely detect real-time emotions holds applications in interactive interfaces, spanning from immersive virtual environments to highly responsive human-computer interactions.