1 research outputs found
Context-Aware Emotion Recognition Networks
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.Comment: International Conference on Computer Vision (ICCV) 201