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Diffstat (limited to 'site/content/pages/datasets/lfw/index.md')
| -rw-r--r-- | site/content/pages/datasets/lfw/index.md | 22 |
1 files changed, 19 insertions, 3 deletions
diff --git a/site/content/pages/datasets/lfw/index.md b/site/content/pages/datasets/lfw/index.md index f52b1be6..e85c7556 100644 --- a/site/content/pages/datasets/lfw/index.md +++ b/site/content/pages/datasets/lfw/index.md @@ -16,7 +16,7 @@ authors: Adam Harvey + Images: 13,233 + Identities: 5,749 + Origin: Yahoo News Images -+ Funding: TBD ++ Funding: (Possibly, partially CIA*)  @@ -67,8 +67,19 @@ Browse or download the geocoded citation data collected for the LFW dataset. - The faces in the LFW dataset were detected using the Viola-Jones haarcascade face detector [^lfw_website] [^lfw-survey] - The LFW dataset is used by several of the largest tech companies in the world including "Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong." [^lfw_pingan] - All images in the LFW dataset were copied from Yahoo News between 2002 - 2004 +<<<<<<< HEAD - In 2014, two of the four original authors of the LFW dataset received funding from IARPA and ODNI for their follow up paper [Labeled Faces in the Wild: Updates and New Reporting Procedures](https://www.semanticscholar.org/paper/Labeled-Faces-in-the-Wild-%3A-Updates-and-New-Huang-Learned-Miller/2d3482dcff69c7417c7b933f22de606a0e8e42d4) via IARPA contract number 2014-14071600010 - The dataset includes 2 images of [George Tenet](http://vis-www.cs.umass.edu/lfw/person/George_Tenet.html), the former Director of Central Intelligence (DCI) for the Central Intelligence Agency whose facial biometrics were eventually used to help train facial recognition software in China and Russia +======= +- In 2014, 2/4 of the original authors of the LFW dataset received funding from IARPA and ODNI for their follow up paper "Labeled Faces in the Wild: Updates and New Reporting Procedures" via IARPA contract number 2014-14071600010 +- The LFW dataset was used Center for Intelligent Information Retrieval, the Central Intelligence Agency, the National Security Agency and National + +TODO (need citations for the following) + +- SenseTime, who has relied on LFW for benchmarking their facial recognition performance, is one the leading provider of surveillance to the Chinese Government [need citation for this fact. is it the most? or is that Tencent?] +- Two out of 4 of the original authors received funding from the Office of Director of National Intelligence and IARPA for their 2016 LFW survey follow up report + +>>>>>>> 13d7a450affe8ea4f368a97ea2014faa17702a4c   @@ -137,9 +148,14 @@ Ignore text below these lines Research -> This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract number 2014-14071600010. +- "In our experiments, we used 10000 images and associated captions from the Faces in the wilddata set [3]." +- "Ths work was supported in part by the Center for Intelligent Information Retrieval, the Central Intelligence Agency, the National Security Agency and National Science Foundation under CAREER award IIS-0546666 and grant IIS-0326249." +- From: "People-LDA: Anchoring Topics to People using Face Recognition" +- This paper was presented at IEEE 11th ICCV conference Oct 14-21 but the main LFW paper "Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments" was not published until 2008 +- 10f17534dba06af1ddab96c4188a9c98a020a459 -"Labeled Faces in the Wild: Updates and New Reporting Procedures" +- This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract number 2014-14071600010. +- From "Labeled Faces in the Wild: Updates and New Reporting Procedures" [^lfw_www]: <http://vis-www.cs.umass.edu/lfw/results.html> [^lfw_baidu]: Jingtuo Liu, Yafeng Deng, Tao Bai, Zhengping Wei, Chang Huang. Targeting Ultimate Accuracy: Face Recognition via Deep Embedding. <https://arxiv.org/abs/1506.07310> |