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+------------
+
+status: published
+title: 00: Introduction
+desc: Introduction to Megapixels
+slug: 00_introduction
+published: 2018-12-15
+updated: 2018-12-15
+authors: Megapixels
+ 
+------------
+
+# MegaPixels
+
++ Posted: Dec. 15
++ Author: Adam Harvey
+
+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.
+
+
+MegaPixels is an online art project that explores the history of facial recognition from the perspective of datasets. To paraphrase the artist Trevor Paglen, whoever controls the dataset controls the meaning. MegaPixels aims to unravel the meanings behind the data and expose the darker corners of the biometric industry that have contributed to its growth. MegaPixels does not start with a conclusion, a moralistic slant, or a 
+
+Whether or not to build facial recognition was a question that can no longer be asked. As an outspoken critic of face recognition I've developed, and hopefully furthered, my understanding during the last 10 years I've spent working with computer vision. Though I initially disagreed, I've come to see technocratic perspective as a non-negotiable reality. As Oren (nytimes article) wrote in NYT Op-Ed "the horse is out of the barn" and the only thing we can do collectively or individually is to steer towards the least worse outcome. Computational communication has entered a new era and it's both exciting and frightening to explore the potentials and opportunities. In 1997 getting access to 1 teraFLOPS of computational power would have cost you $55 million and required a strategic partnership with the Department of Defense. At the time of writing, anyone can rent 1 teraFLOPS on a cloud GPU marketplace for less than $1/day. [^asci_option_red].
+
+
+I hope that this project will illuminate the darker areas of strange world of facial recognition that have not yet received attention and encourage discourse in academic, industry, and . By no means do I believe discourse can save the day. Nor do I think creating artwork can. In fact, I'm not exactly sure what the outcome of this project will be. The project is not so much what I publish here but what happens after. This entire project is only a prologue. 
+
+As McLuhan wrote, "You can't have a static, fixed position in the electric age". And in our hyper-connected age of mass surveillance, artificial intelligece, and unevenly distributed virtual futures the most irrational thing to be is rational. Increasingly the world is becoming a contradiction where people use surveillance to protest surveillance, use 
+
+
+
+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
+
+It was the early 2000s. Face recognition was new and no one seemed sure 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 the borders. It was the future that John Ashcroft demanded with the Total Information Awareness act of the 2003. It was a future that academics helped build. It was a future that celebrities helped build. And it was a future that 
+
+A decade earlier the Department of Homeland Security and the Counterdrug Technology Development Program Office initated a feasibilty study called FERET (FacE REcognition Technology) to "develop automatic face recognition capabilities that could be employed to assist security, intelligence, and law enforcement personnel in the performance of their duties [^feret_website]."
+
+One problem with FERET dataset was that the photos were in controlled settings. For face recognition to work it would have to be used in uncontrolled settings. Even newer datasets such as the Multi-PIE (Pose, Illumination, and Expression) from Carnegie Mellon University included only indoor photos of  cooperative subjects. Not only were the photos completely unrealistic, CMU's Multi-Pie included only 18 individuals and cost $500 for academic use [^cmu_multipie_cost], took years to create, and required consent from every participant.
+
+
+
+## Add progressive gan of FERET
+
+[^multi_domain]: Freitas, Tiago de Pereira; Anjos, Andre ́; Marcel, Sébastien. "Heterogeneous Face Recognition Using Domain Specific Units". 2015. <https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8565895>
+[^met_police]: Sharman, Jon. "Metropolitan Police's facial recognition technology 98% inaccurate, figures show". 2018. <https://www.independent.co.uk/news/uk/home-news/met-police-facial-recognition-success-south-wales-trial-home-office-false-positive-a8345036.html>
+[^asci_option_red]: Calle, Dan. "Supercomptuers". 1997. <http://ei.cs.vt.edu/~history/SUPERCOM.Calle.HTML>
+[^nist_feret]: "Face Recognition Technology (FERET)". <https://www.nist.gov/programs-projects/face-recognition-technology-feret>
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+------------
+
+status: published
+title: From 1 to 100 Pixels
+desc: High resolution insights from low resolution imagery
+tagline: Photographs are for romantics. For the rest of us, it's all about data.  And a photo contains a massive amount of information about who you are.
+image: assets/intro.jpg
+slug: from-1-to-100-pixels
+published: 2018-12-04
+updated: 2018-12-04
+authors: Adam Harvey
+ 
+------------
+ 
+# From 1 to 100 Pixels
+
+### High resolution insights from low resolution data
+
+This post will be about the meaning of "face". How do people define it? How to biometrics researchers define it? How has it changed during the last decade.
+
+What can you know from a very small amount of information?
+
+- 1 pixel grayscale
+- 2x2 pixels grayscale, font example
+- 4x4 pixels
+- 8x8 yotta yotta
+- 5x7 face recognition
+- 12x16 activity recognition
+- 6/5 (up to 124/106) pixels in height/width, and the average is 24/20 for QMUL SurvFace
+- 20x16 tiny faces paper
+- 20x20 MNIST handwritten images <http://yann.lecun.com/exdb/mnist/>
+- 24x24 haarcascade detector idealized images
+- 32x32 CIFAR image dataset
+- 40x40 can do emotion detection, face recognition at scale, 3d modeling of the face. include datasets with faces at this resolution including pedestrian.
+- need more material from 60-100
+- 60x60 show how texture emerges and pupils, eye color, higher resolution of features and compare to lower resolution faces
+- 100x100 0.5% of one Instagram photo
+
+
+Find specific cases of facial resolution being used in legal cases, forensic investigations, or military footage
+
+
+### Research
+
+- 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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diff --git a/site/content/pages/research/index.md b/site/content/pages/research/index.md
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+------------
+
+status: published
+title: Research
+desc: Research blog
+slug: research
+published: 2018-12-15
+updated: 2018-12-15
+authors: Adam Harvey
+sync: false
+
+------------
+
+# Research Blog
+
+### The darkside of datasets and the future of computer vision
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