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Free and open source face recognition with deep neural networks.
- Website: http://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.
- 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.
The following is a BibTeX
and plaintext reference
for the OpenFace GitHub repository.
The reference may change in the future.
The BibTeX entry requires the url LaTeX package.
@misc{amos2016openface,
title = {{OpenFace: Face Recognition with Deep Neural Networks}},
author = {Amos, Brandon and Ludwiczuk, Bartosz and Harkes, Jan and
Pillai, Padmanabhan and Elgazzar, Khalid and Satyanarayanan, Mahadev},
howpublished = {\url{http://github.com/cmusatyalab/openface}},
note = {Accessed: 2016-01-11}
}
Brandon Amos, Bartosz Ludwiczuk, Jan Harkes, Padmanabhan Pillai,
Khalid Elgazzar, and Mahadev Satyanarayanan.
OpenFace: Face Recognition with Deep Neural Networks.
http://github.com/cmusatyalab/openface.
Accessed: 2016-01-11
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.
| Project | Modified | License |
|---|---|---|
| Atcold/torch-TripletEmbedding | No | MIT |
| facebook/fbnn | Yes | BSD |