OpenFace – Free and open source face recognition with deep neural networks.
Imagine the world where you can feel more secure, travel and book faster & safer. No more fraud in casinos in Vegas, no more theft in hotels… Technology, which can improve services, of course once it is not misused.
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.
The 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.
Please use responsibly!
Creators of the OpenFace 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.
The following overview shows the workflow for a single input image of Stallone from the publicly available LFW dataset.
1. Detect faces with a pre-trained models from dlib or OpenCV.
2. 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.
3. 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.
4. 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.
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.