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This repository is the official code for ResEmoteNet. The project is written in Python using PyTorch in MacBook Pro (M2 Pro 10-core CPU and 16-core GPU).

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ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition

PWC PWC PWC PWC

A new network that helps in extracting facial features and predict the emotion labels.

The emotion labels in this project are:

  • Happiness 😀
  • Surprise 😦
  • Anger 😠
  • Sadness ☹️
  • Disgust 🤢
  • Fear 😨
  • Neutral 😐

Table of Content:

Installation

  1. Create a Conda environment.
conda create --n "fer"
conda activate fer
  1. Install Python v3.8 using Conda.
conda install python=3.8
  1. Clone the repository.
git clone https://github.com/ArnabKumarRoy02/ResEmoteNet.git
  1. Install the required libraries.
pip install -r requirement.txt

Usage

Run the file.

cd train_files
python ResEmoteNet_train.py

Checkpoints

All of the checkpoint models for FER2013, RAF-DB and AffectNet-7 can be found here.

Results

  • FER2013:
    • Testing Accuracy: 79.79% (SoTA - 76.82%)
  • CK+:
    • Testing Accuracy: 100% (SoTA - 100%)
  • RAF-DB:
    • Testing Accuracy: 94.76% (SoTA - 92.57%)
  • FERPlus:
    • Testing Accuracy: 91.64% (SoTA - 95.55%)
  • AffectNet (7 emotions):
    • Testing Accuracy: 72.93% (SoTA - 69.4%)
  • ExpW:
    • Testing Accuracy: 75.67%

License

This repository is licensed under the MIT License. See the LICENSE file for more details.

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This repository is the official code for ResEmoteNet. The project is written in Python using PyTorch in MacBook Pro (M2 Pro 10-core CPU and 16-core GPU).

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