Facial Expression Recognition by Deep Learning Models Using Multiple Datasets

Facial Expression Recognition has been studied for many years; however, it remains a challenging task in real-world environments due to complex backgrounds, varying illumination conditions, and online processing issues. In this study, we propose a deep learning model, CAER-Net-RS, by leveraging multiple training datasets. The proposed model integrates three neural networks: the Face Network, the Context Network, and the Adaptive Network. Different datasets are employed for the pretraining of these networks: the facial expression image dataset RAF-DB for the Face Network, the scene image dataset Places365-Standard for the Context Network, and the CAER-S dataset for the Adaptive Network. In the experiment, the proposed model achieved an average recognition accuracy of 85.20% across seven types of facial expressions, compared to 70.92% for the conventional Context-Aware Emotion Recognition Network (CAER-Net).