From c8e7a10be948c2405d46d8c3caf4a8c6675eee29 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Wed, 27 Feb 2019 19:35:54 +0100 Subject: rebuild --- site/public/research/00_introduction/index.html | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) (limited to 'site/public/research') diff --git a/site/public/research/00_introduction/index.html b/site/public/research/00_introduction/index.html index b6cc8e4a..64047134 100644 --- a/site/public/research/00_introduction/index.html +++ b/site/public/research/00_introduction/index.html @@ -42,18 +42,15 @@ -
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.

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Posted
Dec. 15
Author
Adam Harvey

Ignore content below these lines

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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, "we no longer program computers with code, we program them with data".

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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 1, 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. 2.

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 installation in 2016 in NYC, was to build an interactive mirror that showed people if they had been included in the 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.

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About the name

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About the funding

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About me

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About the team

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Conclusion

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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]."

-- cgit v1.2.3-70-g09d2 From d5cc74fd0805f67237a1065cd667e05f6b3616d9 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Thu, 28 Feb 2019 16:07:05 +0100 Subject: comments --- README.md | 36 ++++++++++++++-------- site/public/research/00_introduction/index.html | 7 +++-- .../research/01_from_1_to_100_pixels/index.html | 3 ++ 3 files changed, 31 insertions(+), 15 deletions(-) (limited to 'site/public/research') diff --git a/README.md b/README.md index e46a6289..27dd1b38 100644 --- a/README.md +++ b/README.md @@ -38,28 +38,38 @@ You may need to set the database charset to `utf8mb4` in order to import the CSV ALTER DATABASE megapixels CHARACTER SET = utf8mb4 COLLATE = utf8mb4_unicode_ci; ``` -## Building the site +## Development: automatic rebuilds -The most recently built copy of the site is kept in the repo. This is generated directly from NextCloud. Be mindful that NextCloud will create extra copies of things if there are merge conflicts. +In development, we can watch a bunch of things and rebuild stuff automatically. These rebuild the HTML and the Javascript: ``` -npm install -npm run build -cd megapixels -python cli_faiss.py sync_metadata -python cli_faiss.py build_faiss -python cli_faiss.py build_db -python cli_site.py build +python cli_site.py watch +npm run watch ``` -## Running the site - -On OSX, you must run the server with `pythonw` because of matplotlib. +In addition, run the server, which will serve some HTML (you may need to add index.html to URLs... alas): ``` python cli_flask.py run +``` + +These servers must be running to use all features of the site (face search, etc.) + +``` python `which celery` worker -A app.server.tasks --loglevel=info -E redis-server /usr/local/etc/redis.conf -npm run watch ``` +Note: On OSX, you must run the server with `pythonw` because of matplotlib. + +## Building the site for production + +``` +npm install +npm run build +cd megapixels +python cli_faiss.py sync_metadata +python cli_faiss.py build_faiss +python cli_faiss.py build_db +python cli_site.py build +``` diff --git a/site/public/research/00_introduction/index.html b/site/public/research/00_introduction/index.html index 64047134..395bd268 100644 --- a/site/public/research/00_introduction/index.html +++ b/site/public/research/00_introduction/index.html @@ -42,8 +42,11 @@
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Posted
Dec. 15
Author
Adam Harvey

Ignore content below these lines

-

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, "we no longer program computers with code, we program them with data".

+
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:

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Our relationship to computers has changed. Instead of programming them, we now show them and they figure it out. - Geoffrey Hinton

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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

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 1, 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

diff --git a/site/public/research/01_from_1_to_100_pixels/index.html b/site/public/research/01_from_1_to_100_pixels/index.html index 4446e1be..c11e966e 100644 --- a/site/public/research/01_from_1_to_100_pixels/index.html +++ b/site/public/research/01_from_1_to_100_pixels/index.html @@ -68,6 +68,9 @@
  • 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" 1
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    • "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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    1. NIST 906932. Performance Assessment of Face Recognition Using Super-Resolution. Shuowen Hu, Robert Maschal, S. Susan Young, Tsai Hong Hong, Jonathon P. Phillips↩

      2. -- cgit v1.2.3-70-g09d2