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Reformulation of LBP through Convolutional Filters

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In (Juefei-Xu et al., 2016), the author demonstrates that it is possible to reformulate the LBP through convolutional filters.

Overview

This Jupyter Notebook shows step by step, the process of building a Reformulation of LBP through Convolutional Filters in Python using the TensorFlow framework.

Dependencies

  • PIL
  • os
  • matplotlib
  • numpy
  • tensorflow

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

  • Juefei-Xu, Felix, Vishnu Naresh Boddeti, and Marios Savvides. "Local binary convolutional neural networks." Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on. Vol. 1. 2017.

  • Kanade, Takeo, Jeffrey F. Cohn, and Yingli Tian. "Comprehensive database for facial expression analysis." Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on. 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.

License

Code released under the MIT license.