Emotion Recognition for In-the-wild Videos

Abstract

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.

References in corpus (7)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
• RetinaFace: Single-stage Dense Face Localisation in the Wild
• Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
• Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition
• Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace

Cited by in corpus (2)

• A vector quantized masked autoencoder for audiovisual speech emotion recognition
• An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild