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resemotenet

[IEEE SPL '24] ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition

https://github.com/arnabkumarroy02/resemotenet

Science Score: 39.0%

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Keywords

computer-vision emotion-classification facial-expression-recognition facial-landmarks pytorch
Last synced: 9 months ago · JSON representation

Repository

[IEEE SPL '24] ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition

Basic Info
Statistics
  • Stars: 69
  • Watchers: 2
  • Forks: 17
  • Open Issues: 7
  • Releases: 0
Topics
computer-vision emotion-classification facial-expression-recognition facial-landmarks pytorch
Created about 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

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. bash conda create --n "fer" conda activate fer

  2. Install Python v3.8 using Conda. bash conda install python=3.8

  3. Clone the repository. bash git clone https://github.com/ArnabKumarRoy02/ResEmoteNet.git

  4. Install the required libraries. bash pip install -r requirement.txt

Usage

Run the file. bash 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.

Cite our paper: text @ARTICLE{10812829, author={Roy, Arnab Kumar and Kathania, Hemant Kumar and Sharma, Adhitiya and Dey, Abhishek and Ansari, Md. Sarfaraj Alam}, journal={IEEE Signal Processing Letters}, title={ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition}, year={2024}, pages={1-5}, keywords={Emotion recognition;Feature extraction;Convolutional neural networks;Accuracy;Training;Computer architecture;Residual neural networks;Facial features;Face recognition;Facial Emotion Recognition;Convolutional Neural Network;Squeeze and Excitation Network;Residual Network}, doi={10.1109/LSP.2024.3521321} }

Owner

  • Name: Arnab Kumar Roy
  • Login: ArnabKumarRoy02
  • Kind: user
  • Location: Bongaigaon

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Dependencies

requirements.txt pypi
  • Pillow ==10.3.0
  • dlib ==19.24.2
  • matplotlib ==3.8.3
  • numpy ==1.26.4
  • opencv_python ==4.9.0.80
  • pandas ==2.2.2
  • retina_face ==0.0.14
  • seaborn ==0.13.2
  • torch ==2.1.2
  • torchvision ==0.16.2
  • tqdm ==4.66.1
  • urllib3 ==2.2.1