Facial Expression Recognition in the Wild Using Face Graph and Attention

Abstract: Facial expression recognition is a crucial task in the field of computer vision and human-computer interaction, with applications ranging from affective computing to human behavior analysis. In this study, we propose a method for facial expression recognition utilizing a pre-trained MobileNet model. The MobileNet architecture offers advantages such as computational efficiency and flexibility, making it well-suited for real-time applications on resource-constrained devices. Our approach involves fine-tuning the MobileNet model on a labeled dataset of facial images annotated with corresponding expressions. We preprocess the images to meet the input requirements of the MobileNet model and augment the dataset to improve model generalization. Through a series of experiments, we evaluate the performance of the trained model using metrics such as accuracy, precision, recall, and F1-score. Our results demonstrate the effectiveness of the proposed approach in accurately recognizing expressions from facial images. The trained model shows promising performance, suggesting its potential for practical applications in expression-aware systems, human-computer interaction interfaces, and affective computing platforms. Keywords: Facial Expression Recognition, Face Graph, Attention Mechanism, Deep Learning, Emotion Detection, Human-Computer Interaction