Convolutional Neural Network for Facial Expression Recognition of Basic Emotions

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Alexandra Raibolt ( Lattes | E-mail )

Overview

This Jupyter Notebook shows step by step, the process of building a Convolutional Neural Network for Emotional Expression Recognition in Python using the TensorFlow framework.

In this example we use the Extended CK+ dataset.

Notice:

• The LBCNN model proposed in this work was implemented in Python (version 2.7.12) using the TensorFlow framework (version 1.4.0) using a GPU based architecture, and might not work with other versions.

• The directory where the datasets should stay is not available in GitHub, since it would violate the dataset rules.

Dependencies

• datetime
• scipy.stats
• sklearn.externals
• sklearn.metrics
• gzip
• itertools
• matplotlib
• numpy
• os
• tensorflow
• time

You can install missing dependencies with pip. And install TensorFlow via TensorFlow link.

Usage

1. Install the dependencies;
2. Run Jupyter Notebook in terminal to see the code in your browser.

Credits

• Kanade, Takeo, Yingli Tian, and Jeffrey F. Cohn. "Comprehensive database for facial expression analysis." fg. IEEE, 2000.

• Lucey, Patrick, et al. "The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression." Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on. IEEE, 2010.

• Hvass-Labs

License

Code released under the MIT license.