Free and open source face recognition with deep neural networks.

• Website: https://cmusatyalab.github.io/openface/
• API Documentation
• Join the cmu-openface group or the gitter chat for discussions and installation issues.
• Development discussions and bugs reports are on the issue tracker.

This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by the Intel Corporation, Google, Vodafone, NVIDIA, and the Conklin Kistler family fund. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources.

• batch-represent: Generate representations from a batch of images. Example directory structure.
• demos/web: Real-time web demo.
• demos/compare.py: Demo to compare two images.
• demos/vis-outputs.lua: Demo to visualize the network's outputs.
• demos/classifier.py: Demo to train and use classifiers.
• demos/classifier_webcam.py: Demo to use a trained classifier on a webcam stream.
• evaluation: LFW accuracy evaluation scripts.
• openface: Python library code.
• models: Model directory for openface and 3rd party libraries.
• tests: Tests for scripts and library code, including neural network training.
• training: Scripts to train new OpenFace neural network models.
• util: Utility scripts.

Please cite OpenFace in your publications if it helps your research. The following is a BibTeX and plaintext reference for our OpenFace tech report.

@techreport{amos2016openface,
  title={OpenFace: A general-purpose face recognition
    library with mobile applications},
  author={Amos, Brandon and Bartosz Ludwiczuk and Satyanarayanan, Mahadev},
  year={2016},
  institution={CMU-CS-16-118, CMU School of Computer Science},
}

B. Amos, B. Ludwiczuk, M. Satyanarayanan,
"Openface: A general-purpose face recognition library with mobile applications,"
CMU-CS-16-118, CMU School of Computer Science, Tech. Rep., 2016.

Unless otherwise stated, the source code and trained Torch and Python model files are copyright Carnegie Mellon University and licensed under the Apache 2.0 License. Portions from the following third party sources have been modified and are included in this repository. These portions are noted in the source files and are copyright their respective authors with the licenses listed.