From 8b8d06aec7a71fc9120f5884cda2c0f55781105e Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Thu, 18 Apr 2019 22:21:15 +0200 Subject: rebuild --- site/public/research/00_introduction/index.html | 27 ++++++++++--------------- 1 file changed, 11 insertions(+), 16 deletions(-) (limited to 'site/public/research/00_introduction') diff --git a/site/public/research/00_introduction/index.html b/site/public/research/00_introduction/index.html index ef8a5316..353e3270 100644 --- a/site/public/research/00_introduction/index.html +++ b/site/public/research/00_introduction/index.html @@ -42,15 +42,10 @@
Posted
Dec. 15
Author
Adam Harvey

Facial recognition is a scam.

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It's extractive and damaging industry that's built on the biometric backbone of the Internet.

During the last 20 years commericial, academic, and governmental agencies have promoted the false dream of a future with face recognition. This essay debunks the popular myth that such a thing ever existed.

There is no such thing as face recognition. For the last 20 years, government agencies, commercial organizations, and academic institutions have played the public as a fool, selling a roadmap of the future that simply does not exist. Facial recognition, as it is currently defined, promoted, and sold to the public, government, and commercial sector is a scam.

Committed to developing robust solutions with superhuman accuracy, the industry has repeatedly undermined itself by never actually developing anything close to "face recognition".

There is only biased feature vector clustering and probabilistic thresholding.

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If you don't have data, you don't have a product.

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Yesterday's decision by Brad Smith, CEO of Microsoft, to not sell facial recognition to a US law enforcement agency is not an about face by Microsoft to become more humane, it's simply a perfect illustration of the value of training data. Without data, you don't have a product to sell. Microsoft realized that doesn't have enough training data to sell

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Use Your Own Biometrics First

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If researchers want faces, they should take selfies and create their own dataset. If researchers want images of families to build surveillance software, they should use and distibute their own family portraits.

Motivation

Ever since government agencies began developing face recognition in the early 1960's, datasets of face images have always been central to developing and validating face recognition technologies. Today, these datasets no longer originate in labs, but instead from family photo albums posted on photo sharing sites, surveillance camera footage from college campuses, search engine queries for celebrities, cafe livestreams, or videos on YouTube.

During the last year, hundreds of these facial analysis datasets created "in the wild" have been collected to understand how they contribute to a global supply chain of biometric data that is powering the global facial recognition industry.

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