Facial Emotion Recognition using deep residual learning.
This is my attempt to have a machine learn facial expressions from an image. (Something I seem to have a hard time doing)
The model provided in models/resnet20.h5 is a ResNet with 20 layers.
Accuracy = 67.91 % Loss = 0.8633
Accuracy = 66.48 % Loss = 0.9397
Confusion matrix on Validation data
Install a virtualenv in the project directory
Activate the virtualenv
cd venv/Scripts activate
Install the requirements
pip install -r requirements.txt
Try it out! python server.py
Open browser and visit http://127.0.0.1:5000/
Challenges in Representation Learning: Facial Expression Recognition Challenge (ICML 2013)
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).
The dataset is available for download on Kaggle.
ResNet20 model summary