A Comprehensive Analysis of Integrative Approaches for Emotion Recognition Utilizing Deep Learning Frameworks

Abstract

The study of emotion recognition is progressing quickly and holds considerable potential for use in areas such as healthcare,education,and interactions between humans and computers. Integrative emotion recognition systems that combine information from diverse inputs like facial expressions,vocal tones, body language,and physiological indicators provide enhanced precision and reliability when compared to single-modality systems. This systematic review examines recent advancements, methodologies, and challenges in the design and implementation of multi-modal emotion recognition systems. The review highlights key components, including methods for extracting features,fusion strategies,and deep learning frameworks,emphasizing their use in practical situations,.  The findings underscore the potential of multi-modal systems to provide context-aware, reliable, and inclusive emotion recognition capabilities while addressing limitations such as data privacy concerns, computational complexity, and integration challenges. The present analysis wraps up by exploring potential avenues for future research, emphasizing the need for standardized datasets, cross-cultural studies, and interpretable models to advance the field and its practical applications.