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Current works formulate facial action unit (AU) recognition as a supervisedlearning problem, requiring fully AU-labeled facial images during training. Itis challenging if not impossible to provide AU annotations for large numbers offacial images. Fortunately, AUs appear on all facial images, whether manuallylabeled or not, satisfy the underlying anatomic mechanisms and human behavioralhabits. In this paper, we propose a deep semi-supervised framework for facialaction unit recognition from partially AU-labeled facial images. Specifically,the proposed deep semi-supervised AU recognition approach consists of a deeprecognition network and a discriminator D. The deep recognition network Rlearns facial representations from large-scale facial images and AU classifiersfrom limited ground truth AU labels. The discriminator D is introduced toenforce statistical similarity between the AU distribution inherent in groundtruth AU labels and the distribution of the predicted AU labels from labeledand unlabeled facial images. The deep recognition network aims to minimizerecognition loss from the labeled facial images, to faithfully representinherent AU distribution for both labeled and unlabeled facial images, and toconfuse the discriminator. During training, the deep recognition network R andthe discriminator D are optimized alternately. Thus, the inherent AUdistributions caused by underlying anatomic mechanisms are leveraged toconstruct better feature representations and AU classifiers from partiallyAU-labeled data during training. Experiments on two benchmark databasesdemonstrate that the proposed approach successfully captures AU distributionsthrough adversarial learning and outperforms state-of-the-art AU recognitionwork.

Exploring Adversarial Learning for Deep Semi-Supervised Facial Action Unit Recognition