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, 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
https://github.com/aryaniiit002/Human-Emotion-Recognition.git
cd Human-Emotion-Recognition-
Download the FER-2013 dataset from here.
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If you want to run this program, use this inside your virtual environment:
python main.py- 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.
