  Multi-task learning is an effective learning strategy for deep-learning-based
facial expression recognition tasks. However, most existing methods take into
limited consideration the feature selection, when transferring information
between different tasks, which may lead to task interference when training the
multi-task networks. To address this problem, we propose a novel selective
feature-sharing method, and establish a multi-task network for facial
expression recognition and facial expression synthesis. The proposed method can
effectively transfer beneficial features between different tasks, while
filtering out useless and harmful information. Moreover, we employ the facial
expression synthesis task to enlarge and balance the training dataset to
further enhance the generalization ability of the proposed method. Experimental
results show that the proposed method achieves state-of-the-art performance on
those commonly used facial expression recognition benchmarks, which makes it a
potential solution to real-world facial expression recognition problems.
