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Multi-task learning is an effective learning strategy for deep-learning-basedfacial expression recognition tasks. However, most existing methods take intolimited consideration the feature selection, when transferring informationbetween different tasks, which may lead to task interference when training themulti-task networks. To address this problem, we propose a novel selectivefeature-sharing method, and establish a multi-task network for facialexpression recognition and facial expression synthesis. The proposed method caneffectively transfer beneficial features between different tasks, whilefiltering out useless and harmful information. Moreover, we employ the facialexpression synthesis task to enlarge and balance the training dataset tofurther enhance the generalization ability of the proposed method. Experimentalresults show that the proposed method achieves state-of-the-art performance onthose commonly used facial expression recognition benchmarks, which makes it apotential solution to real-world facial expression recognition problems.

Deep Multi-task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature Sharing