Personalized Multiple Facial Action Unit Recognition through Generative Adversarial Recognition Network

Abstract

Personalized facial action unit (AU) recognition is challenging due to subject-dependent facial behavior. This paper proposes a method to recognize personalized multiple facial AUs through a novel generative adversarial network, which adapts the distribution of source domain facial images to that of target domain facial images and detects multiple AUs by leveraging AU dependencies. Specifically, we use a generative adversarial network to generate synthetic images from source domain; the synthetic images have a similar appearance to the target subject and retain the AU patterns of the source images. We simultaneously leverage AU dependencies to train a multiple AU classifier. Experimental results on three benchmark databases demonstrate that the proposed method can successfully realize unsupervised domain adaptation for individual AU detection, and thus outperforms state-of-the-art AU detection methods.