Pose-Invariant Facial Expression Recognition

Abstract

Pose-invariant facial expression recognition is quite challenging due to variations in facial appearance and self-occlusion caused by head rotations. In this paper, we propose an adversarial multi-view subspace learning method for pose-robust facial expression recognition. Specifically, a deep neural network is trained from face images of a certain pose to learn facial representations. Then, an adversarial strategy is adopted to force statistical similarity among the learned representations from facial images with different poses. Simultaneously, an expression classifier is trained on the learned pose-robust facial representations. Through adversarial learning, the proposed method leverages inherent dependencies among multiple pose facial images to construct pose-robust image representations and a classifier during training. The ensemble method is adopted to combine the predictions of multiple deep neural networks and the common expression classifier, so pose estimation is not required. Experimental results on four benchmark databases demonstrate the superiority of the proposed method to state-of-the-art works.