facial-emotion-recognition

Facial emotion recognition

Using CNN Keras-tensorflow backend

Fer2013

Fer2013 used to use Challenges in Representation Learning: Facial Expression Recognition Challenge in Kaggle.

The data consists of 48x48 pixel grayscale images of faces. The faces have been automatically registered so that the face is more or less centered and occupies about the same amount of space in each image. The task is to categorize each face based on the emotion shown in the facial expression in to one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral).

Those pictures are example of Fer2013 data.

if you want to download Fer2013 data, go here.

Getting Started

• Clone this repo

git clone https://github.com/parkjh688/facial-emotion-recognition.git
cd facial-emotion-recognition

• Installation pip install -r requirements.txt

• train/use model There are two ways to use this code.

1. Start with the model what I trained.
2. Start to train model on your own.

if you want Start with the model what I trained follow this code.

# download trained model
sh model_download.sh

# start facial emotion recognition
python real_time_fer.py

if you want Start to train model on your own follow this code.

"""
your_data_path : the path where you save your data
your_model_path : the path where will you save your model
"""

# start train
python fer.py -d your_data_path -m your_model_path

# start facial emotion recognition
python real_time_fer.py -m your_model_path