openface README

The project's README from github.com/cmusatyalab/openface. As one markdown file: README.md.

OpenFace • Build Status Release License Gitter

Free and open source face recognition with deep neural networks.



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.

What's in this repository?

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.

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.

Project Modified License
Atcold/torch-TripletEmbedding No MIT
facebook/fbnn Yes BSD
dlib-models (68 face landmark detector) No CC0

openface (cmusatyalab/openface)

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这是什么?OpenFace 是一个免费开源的人脸识别工具包,基于深度神经网络构建,由卡内基梅隆大学开发。
这是现成的应用,还是需要接入我自己代码里的东西?它主要是一个 Python 库(即 'openface' 包),底层由 Torch/Lua 神经网络支撑,另外还提供用于比对、分类和实时网页演示的独立示例脚本,而不是一个成品应用。
我该怎么试用?可以从 demos/ 目录中的脚本开始:'compare.py' 用于检查两张照片是否匹配,'classifier.py' 用于训练并运行分类器,'classifierwebcam.py' 用于将训练好的分类器应用到实时摄像头画面上,'batch-represent' 用于为一批图片生成嵌入向量。
使用前我需要先配置好 Torch 和 dlib 吗?需要。识别网络本身运行在 Torch(Lua)上,人脸对齐复用了 dlib 的 68 点关键点检测器,并且提供了 Docker 镜像,因此你不必手动搭建这套环境。
这个项目是给谁用的?它面向的是想要尝试基于深度学习的人脸识别、人脸验证和聚类的研究人员与开发者,而不是寻找即插即用消费级应用的普通用户。
我能训练自己的人脸识别模型,而不用自带的模型吗?可以,training/ 目录里有从零开始训练新 OpenFace 神经网络模型的脚本。
准确率是怎么衡量的?evaluation/ 文件夹中有脚本,可以在 LFW(Labeled Faces in the Wild)数据集上对模型进行基准测试。
这项研究是谁资助的?该项目的开发得到了美国国家科学基金会(NSF)的资助,并获得英特尔、谷歌、沃达丰和英伟达的额外支持。
如果我在工作中使用 OpenFace,需要引用它吗?需要。README 要求凡使用 OpenFace 发表的论文都要引用项目的技术报告,并提供了现成的 BibTeX 条目。
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