ValentinRicher / emotion-recognition-GAN

This project is a semi-supervised approach to detect emotions on faces in-the-wild using GAN

Emotion Recognition with semi-supervised GAN

The goal of this work was to assemble two models used to depict emotions on faces. These models are Action Units and Valence Arousal.
Action Units are facial muscle movements. The combination of these Action Units can be interpreted as an emotion. Valence Arousal is a 2D continuous scale where Valence represents how positive or negative a person is when feeling an emotion and Arousal represents how excited or calm the person is.
Now the possibility to run the code with the facemotion repository !

Prerequisites

• Python 2.7

• Virtual environment manager :

  • virtualenv
  • virtualenvwrapper

• Git LFS
  Git LFS is necessary to download the images contained in the FaceMotion dataset

Setup

1. Clone the project to your environment :

   git clone https://github.com/ValentinRicher/emotion-recognition-GAN.git
   

2. Create the virtual environment :

• with virtualenv

  virtualenv <venv-name>
  

• or with virtualenvwrapper

  mkvirtualenv <venv-name>
  

3. Activate your virtual environment :

• with virtualenv

  source <venv-name>/bin/activate
  

• with virtualenvwrapper

  workon <venv-name>
  

4. Install the libraries :

• if you use a GPU (recommended)

  pip install -R gpu-requirements.txt
  

• if you use a CPU

  pip install -R requirements.txt
  

Usage

• Download the FaceMotion dataset

  python download.py --model xx --img_size yy
  

  This will download the images from the FaceMotion dataset into a ./datasets/facemotion directory if not already done and create the h5py files with the good labels and image sizes.

• Train the model

  python trainer.py --model xx --img_size yy
  

• Evaluate the model

  • if you want to test a specific model

    python evaler.py --checkpoint_path ckpt_p
    

    ckpt_p should be like : BOTH-is_32-bs_64-lr_1.00E-04-ur_5-20190217_145915/train_dir/model-201

  • if you want to test the last model saved

    python evaler.py --train_dir tr_d
    

    tr_d should be like : BOTH-is_32-bs_64-lr_1.00E-04-ur_5-20190217_145915/train_dir/

  • if you want to test all the models in train_dir

    python evaler.py --train_dir tr_d --all
    

For the moment it is only possible to work with 32*32 pixels images because the model architecture for 64*64 and 96*96 are not ready yet

Results

[default model] -> model : BOTH / image size : 32 / batch size : 64 / learning rate : 1e-4 / update rate : 5 / 1 000 000 epochs

In the following grid of 1000 images (20 lines, 50 columns), 1 image is generated for every 1000 epoch

Images created by the Generator during training :

Images created by the Generator during testing :

We can notice that until epoch 400 000, the Generator can create pretty good faces before collapsing

Real images used for training the Discriminator :

Real images used for testing the Discriminator :

To Do

• Add metrics for the real or fake images ✅
• Connect the GAN repo with the dataset repo to create automatic downloading ✅
• Re-organize the facemotion.py to the same level as other .py files ✅
• Use Google Cloud Platform ❌ -> impossible to use a GPU without paying
• Use Google Colab ❌ -> impossible to download the dataset quickly
• Config file to YAML ✅
• Add a file to get the info stored in events files created for TensorBoard ✅
• Do a notice to explain the project and how to use it ✅
• Create an architecture for 64*64 images
• Create an architecture for 96*96 images
• Add an early stopping possibility

Acknowledgments

Parts of the code from https://github.com/gitlimlab/SSGAN-Tensorflow