Facial Expression Recognition Using Deep Learning: A Comprehensive Study on Methodologies and Applications

This paper provides an extensive study on Recognition of facial expressions (FER) using advanced deep learning methods. We highlight the effectiveness of the relationship between long short-term memory and convolutional neural networks (CNNs) (LSTM) networks, which have shown remarkable accuracy in emotion classification tasks. Our proposed model achieved a validation accuracy of 96.24%, significantly outperforming existing models, particularly in recognizing subtle emotional variations. Additionally, we explore the integration of self-attention mechanisms to enhance feature extraction and improve recognition rates across diverse datasets, encompassing FER2013 and RAF-DB. The results highlight the potential of these methodologies in real-world applications, clearing the path for future advancements in emotion AI.