Context-Aware Emotion Recognition Networks
This work addresses the need for more comprehensive emotion recognition systems by incorporating context, though it is incremental as it builds on existing methods with a novel fusion approach.
The authors tackled the problem of limited context in emotion recognition by developing CAER-Net, a deep network that jointly uses facial expressions and context information, achieving improved performance on benchmarks and introducing a new dataset called CAER.
Traditional techniques for emotion recognition have focused on the facial expression analysis only, thus providing limited ability to encode context that comprehensively represents the emotional responses. We present deep networks for context-aware emotion recognition, called CAER-Net, that exploit not only human facial expression but also context information in a joint and boosting manner. The key idea is to hide human faces in a visual scene and seek other contexts based on an attention mechanism. Our networks consist of two sub-networks, including two-stream encoding networks to seperately extract the features of face and context regions, and adaptive fusion networks to fuse such features in an adaptive fashion. We also introduce a novel benchmark for context-aware emotion recognition, called CAER, that is more appropriate than existing benchmarks both qualitatively and quantitatively. On several benchmarks, CAER-Net proves the effect of context for emotion recognition. Our dataset is available at http://caer-dataset.github.io.