Deep Balanced Learning for Long-tailed Facial Expressions Recognition

The analysis of facial expression is a very complex and challenging problem. Most researches for automated Facial Expression Recognition (FER) are mainly based on deep learning networks, rarely considering data imbalance. This paper commits to addressing the long-tail distribution problems among large-scale datasets in wild. Inspired by the continual learning method, we reconstruct multi-subsets first by randomly selecting from head classes and up-sampling tail classes. A pre-trained backbone is then introduced to learn general weights in a repeatedly train-prune fashion. Hereafter, our approach creatively trains a new classifier based on union parameters previously preserved and achieves an outperformance without extra parameters added in, using the gradual-prune technique. The results show that the independent training of classifiers has been a contributing factor. We successfully conduct this experiment with several classic networks, prove its effectiveness in training a deep network on imbalanced dataset. In the face of the poor performance in current FER, we find that domain knowledge is somehow affecting the accuracy of recognition by further exploring the obstacles from the image itself.Code available at https://github.com/Epicghx/FER