Summary of Facial Recognition

Looking for the article?
We have the summary! Get the key insights in just 5 minutes.

Facial Recognition summary
Start getting smarter:
or see our plans

Rating

9

Qualities

  • Comprehensive
  • Applicable
  • Eye Opening

Recommendation

Something like facial recognition technology was created 100 years ago by a French police officer. Now, it’s become omnipresent and mostly unregulated. The facial recognition technology industry is booming and expected to grow to $9.7 billion by 2022, but its technologies raise concerns about privacy and tend to succumb to racial stereotypes. This portfolio of illuminating articles compiled by Meher Ahmad, Adrian Chen, Chris Outcalt and Joy Shan for The California Sunday Magazine includes the viewpoints of people who use facial recognition technology, and how, and why.

 

About the Authors

Meher Ahmad is Associate Editor at The California Sunday Magazine, where Joy Shan is an associate. Blogger Adrian Chen is a former New Yorker staff writer. Chris Outcalt is a freelance writer.

 

Summary

Some form of facial recognition technology goes back to the 19th century, but after the September 11, 2001 terrorist attacks it became ubiquitous.

The first thing that might be called facial recognition technology emerged in France in the late 19th century. It consisted mostly of photographic portraits and measurements meant to help law enforcement officials identify criminals. By the 1960s, facial recognition systems evolved to include using human features to teach computers to recognize faces. Today, computers can teach themselves to recognize faces.

The September 11, 2001 terrorist attacks made facial recognition technologies suddenly more attractive, especially for law enforcement, but its use of facial recognition technology has significant potential downsides. Whether they use facial recognition systems for national border protection or policing city streets, law enforcement officials have no way of insuring that the technology doesn’t encode familiar human prejudices. The data that forms the basis of facial recognition algorithms may suffer from lack of diversity. For this and other reasons, several...


More on this topic

Customers who read this summary also read

Facial Recognition Technology
8
The Malicious Use of Artificial Intelligence
8
HR Trends in 2019
7
How Do You Teach a Car That a Snowman Won’t Walk Across the Road?
8
Can Computers Ever Replace the Classroom?
9
How WeWork Has Perfectly Captured the Millennial Id
8

Related Channels

Comment on this summary