This project aims to classify the emotion on a person's face into one of seven categories, using deep convolutional neural networks. This repository is an implementation of this research paper. The model is trained on the FER-2013 dataset which was published on International Conference on Machine Learning (ICML). Face images with seven emotions - angry, disgusted, fearful, happy, neutral, sad and surprised.
- Python 3, OpenCV 3 or 4, Tensorflow 1 or 2
- 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.
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pretrained model link-
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The folder structure is of the form:
Tensorflow:- 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 CNN.
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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.