History of facial recognition software

At the Super Bowl, law enforcement officials used facial recognition in a major test of the technology. 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. 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. By , about 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. Beginning in , 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. In , the government of Panama, partnering with then-U. Shortly after implementation, the system resulted in the apprehension of multiple Interpol suspects. The FaceFirst implementation at Tocumen remains the largest biometrics installation at an airport to date.

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.

These early pioneers worked to create a computer that could recognize faces. Their initial approach to facial recognition involved manually marking landmarks on the face, like eyes and mouths, and then these were mathematically rotated by the computer to compensate for different poses.

The distances between landmarks were automatically factored in, and compared between images. Over time the technology began to interest others. In , Turk and Pentland presented the eigenface method of facial recognition. This method involved using principal component analysis to take basic images and linearly combine them to reconstruct them. The eigenface method utilized computer skill, matrixes, and high dimensional space to create facial recognition opportunities.

Other technology experts took on the idea and developed software to progress facial recognition forward. Students at universities and professionals in labs all worked to develop new facial recognition tech.

By , facial recognition was becoming more mainstream. At the Super Bowl that year, the police in Florida used face recognition software to search for potential criminals and terrorists. Today facial recognition is used for a variety of reasons, from signing into your phone or computer, to social media, to security.

Social media. For fun, entertaining options, social media platforms utilize facial recognition technology to allow users to apply filters that alter their look. Law enforcement.

The US government has over million photos in their database from drivers license photos. Criminals can be identified, security can be enforced, and more with the help of facial recognition tech. Mobile phone companies. Mobile phones utilize facial recognition to act as another element of security for your device.

Like a fingerprint, your face can be used as a unique identifier, allowing you to unlock or lock your device. They can also use this to match up individuals with tickets, passports, and identify those who caused a problem.

Still, it was the first and foremost step taken by Bledsoe to prove that face recognition was a practical biometric. It was in the s when Harmon, Goldstein, and Lesk made the manual facial recognition system more accurate. The three used 21 facial markers including lip thickness and hair color, to detect faces automatically. Sirovich and Kirby started using linear algebra to the issue of facial recognition in The approach they used was called the Eigenface approach.

The rendering began as a search for low-dimensional facial images representation. The team was able to prove that feature analysis on collected pictures in the database could form a set of basic features.

In , Pentland and Turk worked further on the Eigenfaces approach by finding ways to detect faces within images. They used technological and environmental factors for their approach. In , law enforcement officials applied facial recognition in critical technology testing. When started, Facebook started using a facial recognition feature that helped detect people with featured faces in the photos updated by Facebook users.

While the update created hype in the media industry — Facebook stayed very low key since there was no apparent negative impact on website popularity and usage. The first attempt garnered success, and the FaceFirst expanded into the north terminal facility. How the Technology Is Inherently Racist Not only is the geographic placement of facial recognition technology by law enforcement blatantly racist; the software itself shows significant bias.

The Problematic History of Facial Recognition The roots of facial recognition technology date back to the s, when Woodrow Wilson Bledsoe began developing a system of measurements to classify photos of faces. How the Public Is Fighting Back On the bright side, there has been serious backlash from privacy rights groups, the general public, universities , and some members of Congress against racial bias in the use of the technology.

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