Abstract

In the intersection of artificial intelligence and psychology, sentiment recognition plays a pivotal role in enabling machines to comprehend human emotional expressions. Despite the advancements in artificial intelligence, accurately recognizing and responding to human emotions remains a significant challenge, particularly in diverse social environments. This research addresses this gap by comparing two innovative approaches for facial emotion classification using deep neural networks. The first approach employs a Convolutional Neural Network (CNN) to process raw images directly, while the second approach integrates Dynamic Mode Decomposition (DMD) for feature extraction prior to classification. Both models were trained and evaluated on the FER2013 dataset, and their performances were compared using confusion matrices and ROC curves. The CNN model demonstrated superior accuracy and discriminative capability, particularly in recognizing “Happy” and “Surprise” emotions, whereas the DMD model, despite lower overall accuracy, showed potential in capturing dynamic features. This study’s findings underscore the importance of combining advanced feature extraction techniques with robust neural network architectures to enhance emotion recognition systems’ accuracy and applicability. The implications for human-robot interaction and the development of empathetic AI systems are profound, paving the way for more natural and effective human-computer interactions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.