In this paper, we target on advancing the performance in facial expression recognition (FER) by exploiting omni-supervised learning. The current state of the art FER approaches usually aim to recognize facial expressions in a controlled environment by training models with a limited number of samples. To enhance the robustness of the learned models for various scenarios, we propose to perform omni-supervised learning by exploiting the labeled samples together with a large number of unlabeled data. Particularly, we first employ MS-Celeb-1M as the facial-pool where around 5,822K unlabeled facial images are included. Then, a primitive model learned on a small number of labeled samples is adopted to select samples with high confidence from the facial-pool by conducting feature-based similarity comparison. We find the new dataset constructed in such an omni-supervised manner can significantly improve the generalization ability of the learned FER model and boost the performance consequently. However, as more training samples are used, more computation resources and training time are required, which is usually not affordable in many circumstances. To relieve the requirement of computational resources, we further adopt a dataset distillation strategy to distill the target task-related knowledge from the new mined samples and compressed them into a very small set of images. This distilled dataset is capable of boosting the performance of FER with few additional computational cost introduced. We perform extensive experiments on five popular benchmarks and a newly constructed dataset, where consistent gains can be achieved under various settings using the proposed framework. We hope this work will serve as a solid baseline and help ease future research in FER.
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