Towards a General Deep Feature Extractor for Facial Expression Recognition
This addresses the need for robust and generalizable facial emotion recognition tools, which is incremental as it builds on existing deep learning methods but focuses on cross-dataset generalization.
The paper tackles the problem of poor generalization in deep neural networks for facial expression recognition by proposing DeepFEVER, a deep feature extractor that achieves state-of-the-art results on AffectNet and Google Facial Expression Comparison datasets and generalizes well to unseen datasets like RAF.
The human face conveys a significant amount of information. Through facial expressions, the face is able to communicate numerous sentiments without the need for verbalisation. Visual emotion recognition has been extensively studied. Recently several end-to-end trained deep neural networks have been proposed for this task. However, such models often lack generalisation ability across datasets. In this paper, we propose the Deep Facial Expression Vector ExtractoR (DeepFEVER), a new deep learning-based approach that learns a visual feature extractor general enough to be applied to any other facial emotion recognition task or dataset. DeepFEVER outperforms state-of-the-art results on the AffectNet and Google Facial Expression Comparison datasets. DeepFEVER's extracted features also generalise extremely well to other datasets -- even those unseen during training -- namely, the Real-World Affective Faces (RAF) dataset.