186 lines
8.2 KiB
Markdown
186 lines
8.2 KiB
Markdown
# OpenFace <iframe src="https://ghbtns.com/github-btn.html?user=cmusatyalab&repo=openface&type=star&count=true&size=large" frameborder="0" scrolling="0" width="160px" height="30px"></iframe>
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<center>
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*Free and open source face recognition with
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deep neural networks.*
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</center>
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---
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## News
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+ 2016-01-19: OpenFace 0.2.0 released!
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See [this blog post](http://bamos.github.io/2016/01/19/openface-0.2.0/)
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for more details.
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---
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OpenFace is a Python and [Torch](http://torch.ch) implementation of
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face recognition with deep neural networks and is based on
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the CVPR 2015 paper
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[FaceNet: A Unified Embedding for Face Recognition and Clustering](http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_089.pdf)
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by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google.
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Torch allows the network to be executed on a CPU or with CUDA.
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**Crafted by [Brandon Amos](http://bamos.github.io) in
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[Satya's](https://www.cs.cmu.edu/~satya/) research group at
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Carnegie Mellon University.**
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---
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+ The code is available on GitHub at
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[cmusatyalab/openface](https://github.com/cmusatyalab/openface).
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+ [API Documentation](http://openface-api.readthedocs.org/en/latest/index.html)
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+ Join the
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[cmu-openface group](https://groups.google.com/forum/#!forum/cmu-openface)
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or the
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[gitter chat](https://gitter.im/cmusatyalab/openface)
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for discussions and installation issues.
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+ Development discussions and bugs reports are on the
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[issue tracker](https://github.com/cmusatyalab/openface/issues).
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---
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This research was supported by the National Science Foundation (NSF)
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under grant number CNS-1518865. Additional support
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was provided by the Intel Corporation, Google, Vodafone, NVIDIA, and the
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Conklin Kistler family fund. Any opinions, findings, conclusions or
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recommendations expressed in this material are those of the authors
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and should not be attributed to their employers or funding sources.
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---
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### Isn't face recognition a solved problem?
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No! Accuracies from research papers have just begun to surpass
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human accuracies on some benchmarks.
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The accuracies of open source face recognition systems lag
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behind the state-of-the-art.
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See [our accuracy comparisons](http://cmusatyalab.github.io/openface/models-and-accuracies/)
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on the famous LFW benchmark.
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---
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### Please use responsibly!
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We do not support the use of this project in applications
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that violate privacy and security.
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We are using this to help cognitively impaired users
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sense and understand the world around them.
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---
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# Overview
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The following overview shows the workflow for a single input
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image of Sylvestor Stallone from the publicly available
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[LFW dataset](http://vis-www.cs.umass.edu/lfw/person/Sylvester_Stallone.html).
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1. Detect faces with a pre-trained models from
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[dlib](http://blog.dlib.net/2014/02/dlib-186-released-make-your-own-object.html)
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or
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[OpenCV](http://docs.opencv.org/master/tutorial_py_face_detection.html).
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2. Transform the face for the neural network.
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This repository uses dlib's
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[real-time pose estimation](http://blog.dlib.net/2014/08/real-time-face-pose-estimation.html)
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with OpenCV's
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[affine transformation](http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/warp_affine/warp_affine.html)
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to try to make the eyes and bottom lip appear in
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the same location on each image.
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3. Use a deep neural network to represent (or embed) the face on
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a 128-dimensional unit hypersphere.
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The embedding is a generic representation for anybody's face.
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Unlike other face representations, this embedding has the nice property
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that a larger distance between two face embeddings means
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that the faces are likely not of the same person.
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This property makes clustering, similarity detection,
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and classification tasks easier than other face recognition
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techniques where the Euclidean distance between
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features is not meaningful.
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4. Apply your favorite clustering or classification techniques
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to the features to complete your recognition task.
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See below for our examples for classification and
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similarity detection, including an online web demo.
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# News
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+ [Oct 15, 2015] (Spanish) GenBeta: [OpenFace, un nuevo software de reconocimiento facial, de código abierto](http://www.genbeta.com/actualidad/openface-un-nuevo-software-de-reconocimiento-facial-de-codigo-abierto)
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+ [Oct 15, 2015] TheNextWeb: [Watch this open-source program recognize faces in real time](http://thenextweb.com/dd/2015/10/15/watch-this-open-source-program-recognize-faces-in-real-time/)
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# Blogosphere
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+ [Feb 24, 2016] [Hey Zuck, We Built Your Office A.I. Solution](http://blog.algorithmia.com/2016/02/hey-zuck-we-built-your-facial-recognition-ai/)
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+ [Feb 3, 2016] [RTNiFiOpenFace and WebSocketServer add face recognition to an Apache NiFi video flow](https://richardstechnotes.wordpress.com/2016/02/03/rtnifiopenface-and-websocketserver-add-face-recognition-to-an-apache-nifi-video-flow/)
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+ [Jan 29, 2016] [Integrating OpenFace into an Apache NiFi flow using WebSockets](https://richardstechnotes.wordpress.com/2016/01/29/integrating-openface-into-an-apache-nifi-flow-using-websockets/)
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# Projects using OpenFace
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+ [pyannote/pyannote-video](https://github.com/pyannote/pyannote-video):
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Face detection, tracking, and clustering in videos.
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+ [aybassiouny/OpenFaceCpp](https://github.com/aybassiouny/OpenFaceCpp):
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Unofficial C++ implementation.
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# Citations
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Please cite OpenFace in your publications if it helps your research.
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The following is a [BibTeX](http://www.bibtex.org/) and plaintext reference for our
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[OpenFace tech report](http://reports-archive.adm.cs.cmu.edu/anon/anon/2016/CMU-CS-16-118.pdf).
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```
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@techreport{amos2016openface,
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title={OpenFace: A general-purpose face recognition
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library with mobile applications},
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author={Amos, Brandon and Bartosz Ludwiczuk and Satyanarayanan, Mahadev},
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year={2016},
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institution={CMU-CS-16-118, CMU School of Computer Science},
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}
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B. Amos, B. Ludwiczuk, M. Satyanarayanan,
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"Openface: A general-purpose face recognition library with mobile applications,"
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CMU-CS-16-118, CMU School of Computer Science, Tech. Rep., 2016.
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```
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# Acknowledgements
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+ The fantastic Torch ecosystem and community.
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+ [Alfredo Canziani's](https://github.com/Atcold)
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implementation of FaceNet's loss function in
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[torch-TripletEmbedding](https://github.com/Atcold/torch-TripletEmbedding).
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+ [Nicholas Léonard](https://github.com/nicholas-leonard)
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for quickly merging my pull requests to
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[nicholas-leonard/dpnn](https://github.com/nicholas-leonard/dpnn)
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modifying the inception layer.
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+ [Francisco Massa](https://github.com/fmassa)
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and
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[Andrej Karpathy](http://cs.stanford.edu/people/karpathy/)
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for
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quickly releasing [nn.Normalize](https://github.com/torch/nn/pull/341)
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after I expressed interest in using it.
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+ [Soumith Chintala](https://github.com/soumith) for
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help with the [fbcunn](https://github.com/facebook/fbcunn)
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example code.
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+ [Davis King's](https://github.com/davisking) [dlib](https://github.com/davisking/dlib)
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library for face detection and alignment.
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+ The GitHub issue and pull request templates are inspired from
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[Randy Olsen's](http://www.randalolson.com/) templates at [rhiever/tpot](https://github.com/rhiever/tpot),
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[Justin Abrahms'](https://justin.abrah.ms/) [PR template](https://quickleft.com/blog/pull-request-templates-make-code-review-easier/),
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and
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[Aurelia Moser's](http://algorhyth.ms/) [issue template](https://bl.ocks.org/auremoser/72803ba969d0e61ff070).
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+ Zhuo Chen, Kiryong Ha, Wenlu Hu,
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[Rahul Sukthankar](http://www.cs.cmu.edu/~rahuls/), and
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Junjue Wang for insightful discussions.
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# Licensing
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Unless otherwise stated, the source code and trained Torch and Python
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model files are copyright Carnegie Mellon University and licensed
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under the
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[Apache 2.0 License](https://github.com/cmusatyalab/openface/blob/master/LICENSE).
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Portions from the following third party sources have
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been modified and are included in this repository.
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These portions are noted in the source files and are
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copyright their respective authors with
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the licenses listed.
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Project | Modified | License
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---|---|---|
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[Atcold/torch-TripletEmbedding](https://github.com/Atcold/torch-TripletEmbedding) | No | MIT
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[facebook/fbnn](https://github.com/facebook/fbnn) | Yes | BSD
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