Subject-Based Domain Adaptation for Facial Expression Recognition

Adapting a deep learning (DL) model to a specific target individual is a challenging task in facial expression recognition (FER) that may be achieved using unsupervised domain adaptation (UDA) methods. Although several UDA methods have been proposed to adapt deep FER models across source and target data sets, multiple subject-specific source domains are needed to accurately represent the intra- and inter-person variability in subject-based adaption. In this paper, we consider the setting where domains correspond to individuals, not entire datasets. …

adapt cs.cv data data sets deep learning domain domain adaptation domains multiple recognition unsupervised