CVFeb 13, 2020
Emotion Recognition for In-the-wild Videos
arXiv:2002.05447v115 citations
AI Analysis
This is an incremental improvement for emotion analysis in real-world video applications.
The paper tackled emotion recognition in unconstrained videos by combining ResNet and BLSTM, achieving 64.3% accuracy and 43.4% on a validation metric.
This paper is a brief introduction to our submission to the seven basic expression classification track of Affective Behavior Analysis in-the-wild Competition held in conjunction with the IEEE International Conference on Automatic Face and Gesture Recognition (FG) 2020. Our method combines Deep Residual Network (ResNet) and Bidirectional Long Short-Term Memory Network (BLSTM), achieving 64.3% accuracy and 43.4% final metric on the validation set.