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Compound Expression Recognition via Multi Model Ensemble

Jun Yu, Jichao Zhu, Wangyuan Zhu

TL;DR

This paper proposes a solution based on ensemble learning methods for Compound Expression Recognition that achieves high accuracy on RAF-DB and is able to recognize expressions through zero-shot on certain portions of C-EXPR-DB.

Abstract

Compound Expression Recognition (CER) plays a crucial role in interpersonal interactions. Due to the existence of Compound Expressions , human emotional expressions are complex, requiring consideration of both local and global facial expressions to make judgments. In this paper, to address this issue, we propose a solution based on ensemble learning methods for Compound Expression Recognition. Specifically, our task is classification, where we train three expression classification models based on convolutional networks, Vision Transformers, and multi-scale local attention networks. Then, through model ensemble using late fusion, we merge the outputs of multiple models to predict the final result. Our method achieves high accuracy on RAF-DB and is able to recognize expressions through zero-shot on certain portions of C-EXPR-DB.

Compound Expression Recognition via Multi Model Ensemble

TL;DR

This paper proposes a solution based on ensemble learning methods for Compound Expression Recognition that achieves high accuracy on RAF-DB and is able to recognize expressions through zero-shot on certain portions of C-EXPR-DB.

Abstract

Compound Expression Recognition (CER) plays a crucial role in interpersonal interactions. Due to the existence of Compound Expressions , human emotional expressions are complex, requiring consideration of both local and global facial expressions to make judgments. In this paper, to address this issue, we propose a solution based on ensemble learning methods for Compound Expression Recognition. Specifically, our task is classification, where we train three expression classification models based on convolutional networks, Vision Transformers, and multi-scale local attention networks. Then, through model ensemble using late fusion, we merge the outputs of multiple models to predict the final result. Our method achieves high accuracy on RAF-DB and is able to recognize expressions through zero-shot on certain portions of C-EXPR-DB.
Paper Structure (19 sections, 3 equations, 2 figures, 3 tables)

This paper contains 19 sections, 3 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Data collection
  5. Encoders
  6. Vision Transformer (ViT)
  7. Multi-scale and ocal Attention Network (MANet)
  8. Residual Neural Network (ResNet)
  9. Ensemble
  10. Experiments
  11. Dataset
  12. Implement Details
  13. Evaluation metric
  14. Training setting
  15. Results
  16. ...and 4 more sections

Figures (2)

  • Figure 1: Ensemble
  • Figure 2: Confusion Matrix of Ensemble Models in RAF-DB Compound Expressions