MIDAS: Mixing Ambiguous Data with Soft Labels for Dynamic Facial Expression Recognition
This work addresses the challenge of ambiguous facial expressions in real-world DFER applications, representing an incremental improvement over existing methods.
The authors tackled the problem of recognizing ambiguous facial expressions in dynamic facial expression recognition (DFER) by proposing MIDAS, a data augmentation method that uses soft labels to combine video frames and emotion probabilities. The result showed that models trained with MIDAS outperformed the existing state-of-the-art method on the DFEW dataset.
Dynamic facial expression recognition (DFER) is an important task in the field of computer vision. To apply automatic DFER in practice, it is necessary to accurately recognize ambiguous facial expressions, which often appear in data in the wild. In this paper, we propose MIDAS, a data augmentation method for DFER, which augments ambiguous facial expression data with soft labels consisting of probabilities for multiple emotion classes. In MIDAS, the training data are augmented by convexly combining pairs of video frames and their corresponding emotion class labels, which can also be regarded as an extension of mixup to soft-labeled video data. This simple extension is remarkably effective in DFER with ambiguous facial expression data. To evaluate MIDAS, we conducted experiments on the DFEW dataset. The results demonstrate that the model trained on the data augmented by MIDAS outperforms the existing state-of-the-art method trained on the original dataset.