Due to the large domain discrepancy between the training and testing data and the inaccessibility of annotating sufficient training samples, cross-database facial expression recognition which has more application value remains to be challenging in the literature. Previous researches on this problem are based on shallow features with limited discrimination ability. In this paper, we propose to address this problem with a Deep Emo-transfer Network (DETN). Specifically, maximum mean discrepancy was embedded in the deep architecture to reduce dataset bias. Furthermore, a very common but widely ignored bottleneck in facial expression, imbalanced class distribution, has been taken into account. A learnable class-wise weighting parameter was introduced to our network by exploring class prior distribution on unlabeled data so that the training and testing domains can share similar class distribution. Extensive empirical evidences involving both lab-controlled vs. real-world and small-scale vs. large-scale facial expression databases show that our DETN can yield competitive performances across various facial expression transfer tasks.
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