Image and Audio Based Emotion Detection using Deep Learning

Emotion analysis is a fundamental task in recognizing human behavior and communication having wide range of applications from elderly healthcare, entertainment to human-computer interaction. Emotional analysis is carried out using Convolutional Neural Networks (CNN) for image data. The models are trained on extensive datasets with labeled emotional expressions, enabling them to capture intricate emotional patterns in modalities. This paper presents a comprehensive approach to emotion analysis from image and audio data to augment the accuracy and richness of emotion identification. Our approach's effectiveness is validated using diverse and extensive datasets, ensuring robust generalization across a broad spectrum of emotional expressions and contextual variations. This model contributes to advancing the accuracy and richness of emotion recognition. The paper represents a comprehensive approach to emotion analysis, uniting image data to enhance emotion recognition accuracy.