Context-Aware Emotion Recognition Networks
By Jiyoung Lee, Seungryong Kim, Sunok Kim, Jungin Park, K. Sohn. IEEE International Conference on Computer Vision, 2019
301 citations recorded by the source corpus.
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 separately 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 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.
View Paper: https://www.semanticscholar.org/paper/7f7618f2f315cb06b65925b4d497da30448c4f4d
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Citations in the corpus
- Robust Lightweight Facial Expression Recognition Network with Label Distribution Training (2021, methodology): “To verify the effectiveness of the proposed method, we conduct the experiments on three popular in-the-wild facial expression datasets: RAF-DB (Li and Deng 2018), CAER-S (Lee et al. 2019), and AffectNet (Mollahosseini, Hasani, and Mahoor 2017), and five realistic occlusion and pose variation datasets”