Unsupervised Domain Adaptation with Generative Adversarial Networks for Facial Emotion Recognition

Cross-dataset facial emotion recognition (FER) aims to reduce the discrepancy between the source and the target facial database. The topic is very challenging in FER, where facial features differ across different domains, such as ethnicity, age, gender and environmental condition. In practice, the labels of target facial expression database may be unavailable, making it impossible to fine-tune a pre-trained model via supervised transfer learning. To address this issue, we propose an unsupervised domain adaptation framework with adversarial learning for cross-dataset FER. We perform cross-dataset FER on three well-known publicly available facial expression databases, viz. CK+, Oulu-CASIA, and RAF-DB, showcasing the efficiency of our proposed approach.