  Emotion recognition is a critical aspect of human interaction. This topic
garnered significant attention in the field of artificial intelligence. In this
study, we investigate the performance of convolutional neural network (CNN) and
Modified VGG16 models for emotion recognition tasks across two datasets:
FER2013 and AffectNet. Our aim is to measure the effectiveness of these models
in identifying emotions and their ability to generalize to different and
broader datasets. Our findings reveal that both models achieve reasonable
performance on the FER2013 dataset, with the Modified VGG16 model demonstrating
slightly increased accuracy. When evaluated on the Affect-Net dataset,
performance declines for both models, with the Modified VGG16 model continuing
to outperform the CNN. Our study emphasizes the importance of dataset diversity
in emotion recognition and discusses open problems and future research
directions, including the exploration of multi-modal approaches and the
development of more comprehensive datasets.
