This project aims to classify the emotion on a person's face into one of seven categories, using CNN's. The dataset consists of 35889 48x48 sized face images with various emotions - fearful, angry, neutral, happy, sad, surprised and disgusted.
- Python 3, OpenCV, Tensorflow
- To install the required packages, run
pip install -r requirements.txt.
The repository is currently compatible with tensorflow-2.0 and makes use of the Keras API using the tensorflow.keras library.
- First, clone the repository and enter the folder
git clone https://github.com/waterupto/emotion-detection.git
cd Emotion-detection- If you want to train this model, use:
cd src
python emotions.py --mode train- If you want to view the predictions without training again, you can run the pre-trained model from here:
cd src
python emotions.py --mode display-
The folder structure is of the form:
src:- data (folder)
emotions.py(file)haarcascade_frontalface_default.xml(file)model.h5(file)
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This implementation by default detects emotions on all faces in the webcam feed. With a simple 4-layer CNN, the test accuracy reached 63.2% in 50 epochs.
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First, the haar cascade method is used to detect faces in each frame of the webcam feed.
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The region of image containing the face is resized to 48x48 and is passed as input to the CNN.
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The network outputs a list of softmax scores for the seven classes of emotions.
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The emotion with maximum score is displayed on the screen.
"Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network." Shervin Minaee, Amirali Abdolrashidi - University of California, Riverside