Debunking Myths About Computer Vision – Face Recognition via blog.placemeter.com

We loved this blog post from Placemeter. The link to the original post is here.

http://blog.placemeter.com/2014/10/31/debunking-myths-about-computer-vision/

 

Technology is often over-estimated or under-estimated based on what people see in TV shows and movies. Thanks to Mission Impossible, Jack Bauer, and almost any modern police drama out there computer vision has been recently over-estimated.

 

 

As a CV veteran, I always get amused, and sometimes annoyed by the things that happen “on TV.” I also realize that the impression people get from TV and movies can affect how people perceive technologies like these. So in a new series, I’m going to give readers an idea of what current misconceptions of computer visions are.

Myth Number One:

Instant, Universal Face Recognition

Face recognition is a very complex task. The human eye is extremely well trained at recognizing people—faces are the first things children recognize. But physically, two faces are always very, very close. In short, it is a lot harder to recognize someone than to tell a cow from a car in a picture. Today, face recognition works well in two cases:

  1. if the subject is willing to be recognized
  2. if the “dictionary” of faces is relatively limited

The first is the case when an ATM is using your face to identify you and unlock your card. The algorithm in that case can use several images of your face, use longer exposure time, and usually a large amount of pixels to recognize you.

The second is the case where Facebook or Apple is able to recognize your friends or family in your photos. In that case, the total number of people you are trying to recognize is limited, usually < 100.

facebook-facetags

In any case, you need a good amount of pixels to recognize a face, at least 60×60 in general. And you need good pictures. And you need a limited recognition test.

Face detection algorithms need at least a 60x60 pixel sized face to work.

Law enforcement does use scenarios like those, but it involves a lot of manual work: to recognize a terrorist in a stadium, a cutting edge face recognition algorithm could probably suggest thousands of matches out of the tens of thousands of people present, and an officer would have to manually sift through those. These limits were on full display during the Boston marathon bombing when expensive facial recognition systems failed to identify the Tsarneav brothers.

Based on that, two things would not work: recognizing anyone no matter what on Facebook without the context of who is your friend, or instantly recognizing a terrorist in the crowd in a stadium using a drone.

 

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