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diff --git a/site/content/pages/datasets/msceleb/index.md b/site/content/pages/datasets/msceleb/index.md index d5e52952..4c9f1576 100644 --- a/site/content/pages/datasets/msceleb/index.md +++ b/site/content/pages/datasets/msceleb/index.md @@ -8,8 +8,8 @@ slug: msceleb cssclass: dataset image: assets/background.jpg year: 2015 -published: 2019-2-23 -updated: 2019-2-23 +published: 2019-4-18 +updated: 2019-4-18 authors: Adam Harvey ------------ @@ -19,10 +19,21 @@ authors: Adam Harvey ### sidebar ### end sidebar +The Microsoft Celeb dataset is a face recognition training site made entirely of images scraped from the Internet. According to Microsoft Research who created and published the dataset in 2016, MS Celeb is the largest publicly available face recognition dataset in the world, containing over 10 million images of 100,000 individuals. + +But Microsoft's ambition was bigger. They wanted to recognize 1 million individuals. As part of their dataset they released a list of 1 million target identities for researchers to identity. The identities + +https://www.microsoft.com/en-us/research/publication/ms-celeb-1m-dataset-benchmark-large-scale-face-recognition-2/ + +In 2019, Microsoft CEO Brad Smith called for the governmental regulation of face recognition, an admission of his own company's inability to control their surveillance-driven business model. Yet since then, and for the last 4 years, Microsoft has willingly and actively played a significant role in accelerating growth in the very same industry they called for the government to regulate. This investigation looks look into the [MS Celeb](https://www.microsoft.com/en-us/research/publication/ms-celeb-1m-dataset-benchmark-large-scale-face-recognition-2/) dataset and Microsoft Research's role in creating and distributing the largest publicly available face recognition dataset in the world to both. + + + +to spur growth and incentivize researchers, Microsoft released a dataset called [MS Celeb](https://msceleb.org), or Microsft Celeb, in which they developed and published a list of exactly 1 million targeted people whose biometrics would go on to build + + -https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology -https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm {% include 'dashboard.html' %} @@ -30,11 +41,12 @@ https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-ab ### Additional Information -- The dataset author spoke about his research at the CVPR conference in 2016 <https://www.youtube.com/watch?v=Nl2fBKxwusQ> +- SenseTime https://www.semanticscholar.org/paper/The-Devil-of-Face-Recognition-is-in-the-Noise-Wang-Chen/9e31e77f9543ab42474ba4e9330676e18c242e72 +- Microsoft used it https://www.semanticscholar.org/paper/One-shot-Face-Recognition-by-Promoting-Classes-Guo/6cacda04a541d251e8221d70ac61fda88fb61a70 +- https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology +- https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm ### Footnotes -[^readme]: "readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385. -[^localized_region_context]: Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598. -[^replacement_algorithm]: Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.
\ No newline at end of file +[^brad_smith]: Brad Smith cite
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