Merge branch 'master' of github.com:adamhrv/megapixels_dev

2 files changed, 16 insertions, 11 deletions

diff --git a/site/content/pages/research/00_introduction/index.md b/site/content/pages/research/00_introduction/index.md
index d3ef506b..6fec7ab5 100644
--- a/site/content/pages/research/00_introduction/index.md
+++ b/site/content/pages/research/00_introduction/index.md
@@ -15,6 +15,19 @@ authors: Megapixels
 + Posted: Dec. 15
 + Author: Adam Harvey
 
+
+Ever since the first computational facial recognition research project by the CIA in the early 1960s, data has always played a vital role in the development of our biometric future. Without facial recognition datasets there would be no facial recognition. Datasets are an indispensable part of any artificial intelligence system because, as Geoffrey Hinton points out: 
+> Our relationship to computers has changed. Instead of programming them, we now show them and they figure it out. - [Geoffrey Hinton](https://www.youtube.com/watch?v=-eyhCTvrEtE)
+
+Algorithms learn from datasets. And we program algorithms by building datasets. But datasets aren't like code. There's no programming language made of data except for the data itself. 
+
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+
+Ignore content below these lines
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+

+
 It was the early 2000s. Face recognition was new and no one seemed sure exactly how well it was going to perform in practice. In theory, face recognition was poised to be a game changer, a force multiplier,  a strategic military advantage, a way to make cities safer and to secure borders. This was the future John Ashcroft demanded with the Total Information Awareness act of the 2003 and that spooks had dreamed of for decades. It was a future that academics at Carnegie Mellon Universtiy and Colorado State University would help build. It was also a future that celebrities would play a significant role in building. And to the surprise of ordinary Internet users like myself and perhaps you, it was a future that millions of Internet users would unwittingly play role in creating. 
 
 Now the future has arrived and it doesn't make sense. Facial recognition works yet it doesn't actually work. Facial recognition is cheap and accessible but also expensive and out of control. Facial recognition research has achieved headline grabbing superhuman accuracies over 99.9% yet facial recognition is also dangerously inaccurate. During a trial installation at Sudkreuz station in Berlin in 2018, 20% of the matches were wrong, a number so low that it should not have any connection to law enforcement or justice. And in London, the Metropolitan police had been using facial recognition software that mistakenly identified an alarming 98% of people as criminals [^met_police], which perhaps is a crime itself.
@@ -33,16 +46,6 @@ As McLuhan wrote, "You can't have a static, fixed position in the electric age".
 
 Like many projects, MegaPixels had spent years meandering between formats, unfeasible budgets, and was generally too niche of a subject. The basic idea for this project, as proposed to the original [Glass Room](https://tacticaltech.org/projects/the-glass-room-nyc/) installation in 2016 in NYC, was to build an interactive mirror that showed people if they had been included in the [LFW](/datasets/lfw) facial recognition dataset. The idea was based on my reaction to all the datasets I'd come across during research for the CV Dazzle project. I'd noticed strange datasets created for training and testing face detection algorithms. Most were created in labratory settings and their interpretation of face data was very strict.
 
-About the name
-
-About the funding
-
-About me
-
-About the team
-
-Conclusion
-
 
 
 ### for other post
diff --git a/site/content/pages/research/01_from_1_to_100_pixels/index.md b/site/content/pages/research/01_from_1_to_100_pixels/index.md
index 3a46bccb..409dcf02 100644
--- a/site/content/pages/research/01_from_1_to_100_pixels/index.md
+++ b/site/content/pages/research/01_from_1_to_100_pixels/index.md
@@ -52,4 +52,6 @@ Ideas:
 - NIST report on sres states several resolutions
 - "Results show that the tested face recognition systems yielded similar performance for query sets with eye-to-eye distance from 60 pixels to 30 pixels" [^nist_sres]
 
-[^nist_sres]: NIST 906932. Performance Assessment of Face Recognition Using Super-Resolution. Shuowen Hu, Robert Maschal, S. Susan Young, Tsai Hong Hong, Jonathon P. Phillips
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+[^nist_sres]: NIST 906932. Performance Assessment of Face Recognition Using Super-Resolution. Shuowen Hu, Robert Maschal, S. Susan Young, Tsai Hong Hong, Jonathon P. Phillips
+
+- "Note that we only keep the images with a minimal side length of 80 pixels." and "a face will be labeled as “Ignore” if it is very difficult to be detected due to blurring, severe deformation and unrecognizable eyes, or the side length of its bounding box is less than 32 pixels." Ge_Detecting_Masked_Faces_CVPR_2017_paper.pdf 
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