Context-Based Emotion Recognition using the EMOTIC Dataset

Abstract

Emotion recognition plays a vital role in human-computer interaction, enabling machines to better understand and respond to human emotions. Traditional methods primarily focus on facial expressions or speech for emotion detection. However, context plays a crucial role in accurately interpreting emotions. This paper explores a context-based approach to emotion recognition using the EMOTIC dataset, which includes images labelled with both apparent emotion and contextual information. By employing deep learning models that integrate contextual cues with traditional emotion recognition techniques, we aim to enhance the accuracy and reliability of emotion detection systems. The proposed model achieves significant improvements over baseline methods, demonstrating the importance of context in emotion recognition.