Abstract
As information systems and technologies continue to evolve, there remains a noticeable gap in the efficiency and practicality of data processing algorithms, especially in the field of emotion recognition. This study explores several neural network models designed to classify emotions based on thermal images (thermograms). The dataset used for training included 1,642 images, some of which were generated through augmentation, with all images captured while participants viewed emotionally charged videos. The goal was to recognize six basic emotions: joy, sadness, fear, disgust, anger, and surprise. To identify the most effective architecture, the performance of five models were compared: a standard convolutional neural network (CNN), Quadruplet Network, U-Net, Inception, and SqueezeNet. Each model was trained on the same dataset under consistent conditions. Classification accuracy and validation loss were the main evaluation metrics. In addition, data augmentation and early stopping were applied to improve generalization and prevent overfitting. Among the tested architectures, the Inception model achieved the highest test accuracy of 97.5%, while the Quadruplet Network achieved 96.85% accuracy with a lower validation loss of 0.571, indicating stronger generalization. These results suggest that both models are well-suited for real-time emotion recognition using thermal imaging. The findings highlight the potential of combining infrared data with modern neural architectures to advance emotion detection systems beyond traditional RGB-based methods.