There are several industries benefitting from this technology. Law enforcement agencies are using face recognition to keep communities safer. Retailers are preventing crime and violence. Airports are improving travelers’ convenience and security. And mobile phone companies are using face recognition to provide consumers with new layers of biometric security.
It may seem to some that facial recognition came out of nowhere. But in truth, this technology has been in the works for some time. This post will take a look at the history of face recognition in order to shed light on how this transformative tech came to be, and how it has evolved over time.
Here are some key events in the history of facial recognition:
Manual Measurements by Bledsoe (1960s)
Many would say that the father of facial recognition was Woodrow Wilson Bledsoe. Working in the 1960s, Bledsoe developed a system that could classify photos of faces by hand using what’s known as a RAND tablet, a device that people could use to input horizontal and vertical coordinates on a grid using a stylus that emitted electromagnetic pulses. The system could be used to manually record the coordinate locations of various facial features including the eyes, nose, hairline and mouth.
These metrics could then be inserted in a database. Then, when the system was given a new photograph of an individual, it was able to retrieve the image from the database that most closely resembled that individual. At the time, face recognition was unfortunately limited severely by the technology of the era and computer processing power. However, it was an important first step in proving that face recognition was a viable biometric.
Increased Accuracy with 21 Facial Markers (1970s)
In the 1970s, Goldstein, Harmon, and Lesk were able to add increased accuracy to a manual facial recognition system. They used 21 specific subjective markers including lip thickness and hair color in order to identify faces automatically. As with Bledsoe’s system, the actual biometrics had to still be manually computed.
Eigenfaces (Late 1980s-Early 1990s)
In 1988, Sirovich and Kirby began applying linear algebra to the problem of facial recognition. What became known as the Eigenface approach started as a search for a low-dimensional representation of facial images. Sirovich and Kriby were able to show that feature analysis on a collection of facial images could form a set of basic features. They were also able to show that less than one hundred values were required in order to accurately code a normalized face image.
In 1991, Turk and Pentland expanded upon the Eigenface approach by discovering how to detect faces within images. This led to the first instances of automatic face recognition. Their approach was constrained by technological and environmental factors, but it was a significant breakthrough in proving the feasibility of automatic facial recognition.
FERET Program (1993-2000s)
The Defense Advanced Research Projects Agency (DARPA) and the National Institute of Standards and Technology rolled out the Face Recognition Technology (FERET) program beginning in the 1990s in order to encourage the commercial face recognition market. The project involved creating a database of facial images. The database was updated in 2003 to include high-resolution 24-bit color versions of images. Included in the test set were 2,413 still facial images representing 856 people. The hope was that a large database of test images for facial recognition would be able to inspire innovation, that might result in more powerful facial recognition technology.
Super Bowl XXXV (2002)
At the 2002 Super Bowl, law enforcement officials used facial recognition in a major test of the technology. While officials reported that several “petty criminals” were detected, overall the test was seen as a failure. False positives and backlash from critics proved that face recognition wasn’t quite ready for prime time. One of the big technological limitations at the time was that face recognition did not yet work well in large crowds, functionality that is essential to using face recognition for event security.
Face Recognition Vendor Tests (2000s)
The National Institute of Standards and Technology (NIST) began Face Recognition Vendor Tests (FRVT) in the early 2000s. Building on FERET, FRVTs were designed to provide independent government evaluations of facial recognition systems that were commercially available, as well as prototype technologies. These evaluations were designed to provide law enforcement agencies and the U.S. government with information necessary to determine the best ways to deploy facial recognition technology.
Law Enforcement Forensic Database (2009)
In 2009, the Pinellas County Sherriff’s Office created a forensic database that allowed officers to tap into the photo archives of the state’s Department of Highway Safety and Motor Vehicles (DHSMV). By 2011, about 170 deputies had been outfitted with cameras that let them take pictures of suspects that could be cross-checked against the the database. This resulted in more arrests and criminal investigations than would have otherwise been possible.
Social Media (2010-Present)
Beginning in 2010, Facebook began implementing facial recognition functionality that helped identify people whose faces may be featured in the photos that Facebook users update daily. While the feature was instantly controversial with the news media, sparking a slew of privacy-related articles, Facebook users at large did not seem to mind. Having no apparent negative impact on the website’s usage or popularity, more than 350 million photos are uploaded and tagged using face recognition each day.
First Major Installation of Face Recognition in an Airport (2011)
In 2011, the government of Panama, partnering with then-U.S. Secretary of Homeland Security Janet Napolitano, authorized a pilot program of FaceFirst’s facial recognition platform in order to cut down on illicit activity in Panama’s Tocumen airport (known as a hub for drug smuggling and organized crime).
Shortly after implementation, the system resulted in the apprehension of multiple Interpol suspects. Pleased with the success of the initial deployment, FaceFirst expanded into the facility’s north terminal. The FaceFirst implementation at Tocumen remains the largest biometrics installation at an airport to date.
Osama Bin Laden Identified (2011)
Face recognition has been used increasingly for forensics by law enforcement and military professionals. It is often the most effective way to positively identify dead bodies. In fact, facial recognition was used to help confirm the identity of Osama bin Laden after he was killed in a U.S. raid.
Law Enforcement Agencies Adopt Mobile Face Recognition (2014)
Beginning in 2014, The Automated Regional Justice Information System (ARJIS), began supplying partner agencies with FaceFirst’s mobile platform supporting face recognition for law enforcement. ARJIS, a complex criminal justice enterprise network that promotes information and data sharing among local, state and federal law enforcement agencies, wanted to solve a critical problem: instant identification for people who had no ID or did not want to be identified. Some of the agencies that started using mobile face recognition to identify suspects in the field include San Diego police, DOJ, FBI, DEA, CBP and U.S. Marshalls.
Face Recognition “Inevitable” for Retail (2017)
As face recognition is adopted by retail faster than any other industry, experts are taking note. In a recent webinar, D&D Daily Publisher and Editor Gus Downing stated that face recognition is on an “inevitable path to retail adoption.” Downing, considered one of the foremost loss prevention thought leaders, is just one expert that now sees massive advantages for retailers who use a face recognition system.
iPhone X (2017)
Apple released the iPhone X in 2017, advertising face recognition as one of it’s primary new features. The face recognition system in the phone is used for device security. The new model of iPhone sold out almost instantly, proving that consumers now accept facial recognition as the new gold standard for security.