Evaluating the effectiveness of selected tools in recognizing emotions from facial photos

Emotion recognition from facial images has become a key area in computer vision and affective computing. Deep learning models such as convolutional neural networks and vision transformers have shown high potential in this domain. In this study, the performance of two representative architectures, ResNet-50, a convolutional neural networks based model, and ViT-B/16, a transformer-based model, is evaluated on the widely used Facial Expression Recognition 2013 dataset. Both models are trained using data augmentation and regularization techniques to enhance generalization. Their effectiveness is assessed using metrics including accuracy, precision, recall, and F1-score, alongside a detailed examination of confusion matrices. The observed differences in classification performance across emotion categories highlight the influence of architectural design on model behavior. The obtained results serve as a reference point for selecting appropriate deep learning architectures.