  Current facial expression recognition methods fail to simultaneously cope
with pose and subject variations.
  In this paper, we propose a novel unsupervised adversarial domain adaptation
method which can alleviate both variations at the same time. Specially, our
method consists of three learning strategies: adversarial domain adaptation
learning, cross adversarial feature learning, and reconstruction learning. The
first aims to learn pose- and expression-related feature representations in the
source domain and adapt both feature distributions to that of the target domain
by imposing adversarial learning. By using personalized adversarial domain
adaptation, this learning strategy can alleviate subject variations and exploit
information from the source domain to help learning in the target domain.
  The second serves to perform feature disentanglement between pose- and
expression-related feature representations by impulsing pose-related feature
representations expression-undistinguished and the expression-related feature
representations pose-undistinguished.
  The last can further boost feature learning by applying face image
reconstructions so that the learned expression-related feature representations
are more pose- and identity-robust.
  Experimental results on four benchmark datasets demonstrate the effectiveness
of the proposed method.
