Abstract
Facial expression recognition (FER) has garnered significant attention due to advances in artificial intelligence, particularly in applications like driver monitoring, healthcare, and human-computer interaction, which benefit from deep learning techniques. The motivation of this research is to address the challenges of accurately recognizing emotions despite variations in expressions across emotions and similarities between different expressions. In this work, we propose an early fusion approach that combines features from visible and infrared modalities using publicly accessible VIRI and NVIE databases. Initially, we developed single-modality models for visible and infrared datasets by incorporating an attention mechanism into the ResNet-18 architecture. We then extended this to a multi-modal early fusion approach using the same modified ResNet-18 with attention, achieving superior accuracy through the combination of convolutional neural network (CNN) and transfer learning (TL). Our multi-modal approach attained 84.44% accuracy on the VIRI database and 85.20% on the natural visible and infrared facial expression (NVIE) database, outperforming previous methods. These results demonstrate that our single-modal and multi-modal approaches achieve state-of-the-art performance in FER.