Facial Emotion Recognition by CNN Combined Ensemble Model

Studying emotions can provide important information about a person's mental state. According to research, more than 50% of a person's current emotions can be identified from the human face. In this research, we propose an ensemble model for emotion recognition from facial images, which is obtained by combining the results obtained by retraining previously trained convolutional neural networks on a new and high-quality FaceEmocDS dataset. The methodological advantage of the ensemble model we propose is that the combination of VGG19, ResNet50, and DenseNet121 models allows us to take advantage of the strengths of each architecture: the ability to extract detailed features of VGG19, the stable learning process of ResNet50 through residual connections, and the efficiency of feature reuse of DenseNet121. This approach improves the results of individual models, increasing the accuracy to 85.66% . The FaceEmocDS dataset consists of 72,412 images and includes eight emotion classes, including a unique “contempt” class. The results show significant superiority when compared to other datasets (FER2013, AffectNet, CK+) and studies.