Domain Adaptation for Facial Expression Classifier via Domain Discrimination and Gradient Reversal
This work addresses domain adaptation for FER, which is incremental as it builds on existing methods to enhance model robustness in applications like health-care and security.
The paper tackles the problem of improving facial expression recognition (FER) across different domains by proposing a new architecture that integrates domain discrimination loss regularization, achieving competitive performance in unsupervised domain adaptation scenarios.
Bringing empathy to a computerized system could significantly improve the quality of human-computer communications, as soon as machines would be able to understand customer intentions and better serve their needs. According to different studies (Literature Review), visual information is one of the most important channels of human interaction and contains significant behavioral signals, that may be captured from facial expressions. Therefore, it is consistent and natural that the research in the field of Facial Expression Recognition (FER) has acquired increased interest over the past decade due to having diverse application area including health-care, sociology, psychology, driver-safety, virtual reality, cognitive sciences, security, entertainment, marketing, etc. We propose a new architecture for the task of FER and examine the impact of domain discrimination loss regularization on the learning process. With regard to observations, including both classical training conditions and unsupervised domain adaptation scenarios, important aspects of the considered domain adaptation approach integration are traced. The results may serve as a foundation for further research in the field.