The Rise and Fall of Facial Recognition
A Brief Timeline
The ability of facial recognition technology to identify potential criminals was once regarded as a promising tool for advancing public and private security. Over the years, the software has faced mounting controversy pertaining to bias and privacy issues. How and why did the public start to lose trust in facial recognition?
On Facial Recognition
View Timeline to Learn More
In 1964 and 1965, Woody Bledsoe, Charles Bisson, and Helen Chan Wolf became pioneers of facial recognition. The trio worked together to research and test how to use computers for recognizing a human face.
1960s: Facial Recognition Tech First Emerges
In the 1990s, the Face Recognition Technology (FERET) program officially launched, creating a database of over 1,000 people. The program aimed to encourage commercial uses for facial recognition.
1990s: FERET Program is Launched
An MIT report released in 2018 confirmed that facial recognition software had over a 30% higher likelihood of error with dark-skinned women when compared to light-skinned men.
2018: MIT Study Released on Facial Recognition Bias
In 2019, San Francisco became the first city to ban the use of facial recognition software stating that it was incompatible with a healthy democracy. Shortly after, other cities began falling suit with their own bans.
2019: San Franciso Bans Facial Recognition Software
Today, nearly twenty US cities have banned law enforcement and other agencies from using facial recognition software. As the bans continue to increase, so does the public's distrust of the software's capabilities.
2021 and Beyond: The Controversy Continues
AI bias is a complex issue that involves not just algorithmic, but human biases as well. AI video analytics serves as a preferable alternative to facial recognition because the algorithms are designed to detect objects and actions—not individuals. The bottom line: privacy and safety do not have to be compromised in order to achieve smarter surveillance.
Looking Beyond Facial Recognition to Avoid Bias