Spatial and Temporal Networks for Facial Expression Recognition in the Wild Videos
arXiv:2107.05160
Abstract
The paper describes our proposed methodology for the seven basic expression classification track of Affective Behavior Analysis in-the-wild (ABAW) Competition 2021. In this task, facial expression recognition (FER) methods aim to classify the correct expression category from a diverse background, but there are several challenges. First, to adapt the model to in-the-wild scenarios, we use the knowledge from pre-trained large-scale face recognition data. Second, we propose an ensemble model with a convolution neural network (CNN), a CNN-recurrent neural network (CNN-RNN), and a CNN-Transformer (CNN-Transformer), to incorporate both spatial and temporal information. Our ensemble model achieved F1 as 0.4133, accuracy as 0.6216 and final metric as 0.4821 on the validation set.
References in corpus (6)
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
- Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network
- Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study
- Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework
- Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace