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). This dataset consists of 35887 grayscale, 48x48 sized face images with seven emotions - angry, disgusted, fearful, happy, neutral, sad and surprised.
- Python 3.6, OpenCV 3 or 4, Tensorflow, TFlearn
- To install the required packages, run
pip install -r requirements.txt.
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Download the trained model files from here, extract it and copy the files into the current working directory.
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To run the program to detect emotions only in one face, type
python model.py singleface. -
To run the program to detect emotions on all faces close to camera, type
python model.py multiface. Note that this sometimes generates incorrect predictions. -
The folder structure is of the form:
TFLearn:- emojis (folder)
model.py(file)multiface.py(file)singleface.py(file)model_1_atul.tflearn.data-00000-of-00001(file)model_1_atul.tflearn.index(file)model_1_atul.tflearn.meta(file)haarcascade_frontalface_default.xml(file)
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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.
- "Challenges in Representation Learning: A report on three machine learning contests." I Goodfellow, D Erhan, PL Carrier, A Courville, M Mirza, B
Hamner, W Cukierski, Y Tang, DH Lee, Y Zhou, C Ramaiah, F Feng, R Li,
X Wang, D Athanasakis, J Shawe-Taylor, M Milakov, J Park, R Ionescu, M Popescu, C Grozea, J Bergstra, J Xie, L Romaszko, B Xu, Z Chuang, and Y. Bengio. arXiv 2013.