Advances in Facial Emotion Recognition: Deep Learning Approaches and Future Prospects

Facial Emotion Recognition (FER) has evolved significantly with the advent of deep learning techniques, offering novel approaches to interpret and process facial expressions. This paper reviews recent advancements in FER, emphasizing the role of deep neural network architectures and their application across diverse fields including security, health, and human-machine interfaces. We analyze various methodologies, technological advancements, challenges, and future trends in FER, providing insights into the current state-of-the-art. We conclude by discussing the limitations of current FER systems and propose future directions for research, particularly in extending emotion recognition beyond basic emotions and incorporating multimodal approaches.