OpenFace

Free and open source face recognition with deep neural networks.

News

OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA.

Crafted by Brandon Amos, Bartosz Ludwiczuk, and Mahadev Satyanarayanan.

The code is available on GitHub at cmusatyalab/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.

Isn't face recognition a solved problem?

No! Accuracies from research papers have just begun to surpass human accuracies on some benchmarks. The accuracies of open source face recognition systems lag behind the state-of-the-art. See our accuracy comparisons on the famous LFW benchmark.

Please use responsibly!

We do not support the use of this project in applications that violate privacy and security. We are using this to help cognitively impaired users sense and understand the world around them.

Overview

The following overview shows the workflow for a single input image of Sylvestor Stallone from the publicly available LFW dataset.

Detect faces with a pre-trained models from dlib or OpenCV. Transform the face for the neural network. This repository uses dlib's real-time pose estimation with OpenCV's affine transformation to try to make the eyes and bottom lip appear in the same location on each image. Use a deep neural network to represent (or embed) the face on a 128-dimensional unit hypersphere. The embedding is a generic representation for anybody's face. Unlike other face representations, this embedding has the nice property that a larger distance between two face embeddings means that the faces are likely not of the same person. This property makes clustering, similarity detection, and classification tasks easier than other face recognition techniques where the Euclidean distance between features is not meaningful. Apply your favorite clustering or classification techniques to the features to complete your recognition task. See below for our examples for classification and similarity detection, including an online web demo.

Posts About OpenFace

Notable Relevant Projects

BrandonJoffe/home_surveillance: Home surveillance dashboard that uses OpenFace for face recognition.

davidsandberg/facenet: FaceNet TensorFlow implementation.

pyannote/pyannote-video: Face detection, tracking, and clustering in videos using OpenFace.

aybassiouny/OpenFaceCpp: Unofficial OpenFace C++ implementation and bindings.

Citations

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.

Acknowledgements

Licensing

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.