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Blending in, photo by @turutututuu

A Primer for Facial Recognition Technologies

A summary of the primer by the Algorithmic Justice League

I recently read a document that summarised well some of the current challenges within facial recognition technology. It was a short and concise document, still I thought to myself it might be useful to make a shorter summary.

  1. provide space for people to voice concerns and experiences with coded discrimination
  2. develop practices for accountability during the design, development, and deployment phases of coded systems.

So what is facial recognition technology or FTR for short?

They define Facial Recognition Technologies (FRTs) to be a set of digital tools used to perform tasks on images or videos of human faces.

  1. What kind of face is shown in the image?
  2. Whose face is shown in the image?
  1. Face identification: attempts to answer the question, “Whose face is this?” Face identification software can only match the image of a face to a person for whom it already has some appearance information. The set of people for whom an application has stored appearance information is called the gallery. Simply put, this is the set of people that a face identification system could possibly identify. A typical example of a gallery would be the set of people who work in a secured location, such as a private office building.

Where is FTR used?

The short report mentions that FTR is being used in several place already.

  • Consumer Products.
  • Events.
  • Housing.
  • Police Departments.
  • Places of Worship.
  • Schools.
  • Stores.
  • Transportation.
  • Workplaces.
  1. Enrolment.
  2. The digital representation of a face.
  3. Comparison.
  4. Matching decision.
  • True negative (or true mismatch). In addition to verifying and identifying a unique individual, systems should also correctly reject faces that do not match.
  • False positive (or false match). A false positive means the wrong person is deemed to be a match. Depending on the application, the consequences of such an incorrect decision can vary.
  • False negative (or false mismatch). Rejecting the correct person results in a false negative outcome (or false mismatch). For facial verification used for fraud detection, a false negative can mean an individual is denied access to a service or opportunity.

How accurate is FTR?

There are different way to evaluate this and the report lists a few.

  • Real-world performance and benchmark results.

“…what are effective alternatives to using benchmarks and metrics in order to decide if a specific facial recognition technology is appropriate for deployment for a particular application in a targeted population?”

There is a need for questions that go beyond accuracy and technical considerations.

  • Privacy.
  • Consent.
  • Legality.
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A sign at a black lives matter protest in Atlanta, photo by @mcoswalt

Written by

AI Policy and Ethics at www.nora.ai. Student at University of Copenhagen MSc in Social Data Science. All views are my own. twitter.com/AlexMoltzau

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