EmoNeXt: an Adapted ConvNeXt for facial Emotion Recognition

This repository contains the code referenced in the paper: "EmoNeXt: an Adapted ConvNeXt for facial Emotion Recognition".

Abstract

Facial expressions play a crucial role in human communication serving as a powerful and impactful means to express a wide range of emotions. With advancements in artificial intelligence and computer vision, deep neural networks have emerged as effective tools for facial emotion recognition. In this paper, we propose EmoNeXt, a novel deep learning framework for facial expression recognition based on an adapted ConvNeXt architecture network. We integrate a Spatial Transformer Network (STN) to focus on feature-rich regions of the face and Squeeze-and-Excitation blocks to capture channel-wise dependencies. Moreover, we introduce a self-attention regularization term, encouraging the model to generate compact feature vectors. We demonstrate the superiority of our model over existing state-of-the-art deep learning models on the FER2013 dataset regarding emotion classification accuracy.

Quick start

1. Install CUDA

2. Install PyTorch 1.13 or later

3. Install dependencies

   pip install -r requirements.txt

4. Download the data and run training:

   python scripts/download_dataset.py
   python train.py \
       --dataset-path='FER2013' \
       --batch-size=64 --lr=0.0001 \
       --epochs=300 \
       --amp \
       --in_22k \
       --num-workers=1 \
       --model-size='tiny'

Comments

Our codebase builds heavily on Facebook's ConvNeXt. Thanks for open-sourcing!

Citation

Please use the following bibtex entry:

  @inproceedings{el2023emonext,
    title={EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition},
    author={El Boudouri, Yassine and Bohi, Amine},
    booktitle={2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP)},
    pages={1--6},
    year={2023},
    organization={IEEE}
  }