This project aims to classify the emotion on a person's face into one of seven categories, using deep convolutional neural networks. The model is trained on the FER-2013 dataset which was published on International Conference on Machine Learning (ICML). This dataset consists of 35887 grayscale, 48x48 sized face images with seven emotions - angry, disgusted, fearful, happy, neutral, sad and surprised.
- Python 3, OpenCV 3 or 4, Tensorflow 1 or 2
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Download the FER-2013 dataset from here and unzip it inside the folder and create a "data" Folder.
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If you want to train this model or train after making changes to the model, use
python emotions.py --mode train. -
If you want to view the predictions without training again, then run "python emotions.py --mode display".
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The folder structure is of the form:
- 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.
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With a simple 4-layer CNN, the test accuracy peaked at around 50 epochs at an accuracy of 63.2%.
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First, we use haar cascade 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 ConvNet.
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The network outputs a list of softmax scores for the seven classes.
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The emotion with maximum score is displayed on the screen.