'Faceless Recognition System' Can Identify You Even When You Hide Your Face
'Faceless Recognition System' Can Identify You Even When
You Hide Your Face
Written by JOSHUA KOPSTEIN August 7, 2016 01:00 PM EST
With widespread adoption among law enforcement,
advertisers, and even churches, face recognition has undoubtedly become one of
the biggest threats to privacy out there.
By itself, the ability to instantly identify anyone just
by seeing their face already creates massive power imbalances, with serious
implications for free speech and political protest. But more recently,
researchers have demonstrated that even when faces are blurred or otherwise
obscured, algorithms can be trained to identify people by matching
previously-observed patterns around their head and body.
In a new paper uploaded to the ArXiv pre-print server,
researchers at the Max Planck Institute in Saarbrücken, Germany demonstrate a
method of identifying individuals even when most of their photos are un-tagged
or obscured. The researchers' system, which they call the “Faceless Recognition
System,” trains a neural network on a set of photos containing both obscured
and visible faces, then uses that knowledge to predict the identity of obscured
faces by looking for similarities in the area around a person's head and body.
The accuracy of the system varies depending on how many
visible faces are available in the photo set. Even when there are only 1.25
instances of the individual's fully-visible face, the system can identify an
obscured faced with 69.6 percent accuracy; if there are 10 instances of an
individual's visible face, it increases to as high as 91.5 percent.
In other words, even if you made sure to obscure your
face in most of your Instagram photos, the system would have a decent chance
identifying you as long as there are one or two where your face is fully
visible.
It turns out this becomes a lot harder to do using sets
of photos from “across events,” or when factors like illumination and the
person's clothing have changed. The researchers found that when identifying
faces obscured by black squares across events, the system's performance drops
dramatically from 47.4 percent to 14.7 percent—but even that is three times
more accurate than the “naive” method of identifying obscured faces through
blind prediction, the researchers note.
In the past, Facebook has shown its face recognition
algorithms can predict the identity of users when they obscure their face with
83% accuracy, using cues such as their stance and body type. But the
researchers say their system is the first to do so using a trainable system
that uses a full range of body cues surrounding blurred and blacked-out faces.
“From a privacy perspective, the results presented here
should raise concern,” the researchers write. “It is very probable that
undisclosed systems similar to the ones described here already operate online.
We believe it is the responsibility of the computer vision community to
quantify, and disseminate the privacy implications of the images users share
online.”
Comments
Post a Comment