Advancing Multimodal Emotion Detection: A Comprehensive Survey of Techniques, Challenges and Applications

The rapid advancement of human-computer interaction has underscored the need for a deeper understanding of human emotions, particularly in contexts where emotions are conveyed through body language, facial expressions, and auditory signals. This survey provides a comprehensive examination of recent developments in emotion recognition, with a focus on integrating spatial features from video data. Specifically, we explore advancements in deep learning architectures, including Temporal Convolutional Networks (TCNs), 3D Convolutional Neural Networks (3D-CNNs), and Multimodal Transformer Networks (MTNs). A key contribution of this study lies in its early hybrid exploration of emotion recognition techniques, integrating multiple methodologies to enhance accuracy and adaptability. Critical challenges such as latency, context awareness, and system flexibility in real-time applications are analysed, with particular emphasis on social and environmental factors influencing emotional perception. Furthermore, we discuss novel contextual attention mechanisms aimed at refining recognition accuracy and propose advancements in real-time processing pipeline optimization to meet the growing demand for low-latency adaptive systems. By bridging the knowledge gap between traditional single-method approaches and state-of-theart multimodal systems, this study highlights emerging research directions and key challenges in the field. The transformative potential of emotion recognition frameworks is examined across various applications, including telemedicine, education, gaming, and customer service. This work serves as a valuable resource for researchers and practitioners seeking to develop scalable, efficient, and high-precision emotion recognition systems for real-world deployment.