siliconchip.com.au Australia’s electronics magazine April 2019 21
Fig.14: an image processed (right) with D-ID’s software to
protect the biometric data of the individual that is in the
original image (left). Facial recognition systems cannot
recognise the individual in the processed image, even
though it looks almost the same to a human.
In May 2018, Taylor Swift used facial recognition at
one of her concerts to identify any of hundreds of stalkers
she claims to have. See the Rolling Stone article at: http://
siliconchip.com.au/link/aandThwarting facial recognition
Many people who have nothing to hide still have con-
cerns about being photographed or recorded without their
knowledge. Their biometric data could be added to a data-
base, which may cause problems for them in future, or their
presence at certain locations could be logged to some central
“Big Brother” database and used to track their movements.
One particular concern is the unauthorised use of their im-
age in identity theft, or to gain access to restricted areas or de-
vices such as smartphones protected with facial ID security.
As a result, Israeli company D-ID (www.deidentification.
co) has developed a method to process pictures and videos
to render them unidentifiable by facial recognition systems.
Pictures to be protected might be staff pictures on company
websites, for example. The images are subtly altered in a way
which is barely or not discernible to a person but prohibits
identification by a machine (Fig.14).
There are also legal ramifications of this because according
to the European Union’s General Data Protection Regulation
(GDPR), currently in force, face images are regarded as “sen-
sitive personal information” and organisations are required
to protect this data or face penalties (no pun intended!).
Another approach to thwarting unwanted facial recog-
nition involves the use of 3D printed eyeglass frames and
this was the subject of an academic paper; see siliconchip.
com.au/link/aanc
Unlike the 3D printed glasses that were the subject of this
paper, regular glasses can be ignored by more advanced face
recognition systems. The Japanese Government’s National
Institute of Informatics (NII) developed “privacy visors” in
2015 to thwart unwanted facial recognition.
Many other methods have been developed to thwart un-
wanted facial recognition such as unusual facial makeup
or clothing with printed faces etc, but one would hardly
go unnoticed!that are undesirable but not necessarily criminal can be
punished in other ways, such as having restricted travel
or being prevented from buying certain products.
Facial recognition and tracking is combined with all re-
cords pertaining to a person such as a criminal record (if
any), medical records, travel bookings, online purchases,
social media comments, friends on social media or else-
where with the view of tracking where an individual is,
who they are associating with, what they are up to, where
they are heading, etc.
Apart from Xue Liang’s use of physical records, it com-
bines artificial intelligence, data mining and deep learning
technologies to further enhance the system’s effectiveness.
In addition to government surveillance cameras, the sys-
tem also integrates private security cameras from places
such as apartment blocks and shopping malls.
For more information, see this video from the Washing-
ton Post titled “How China is building an all-seeing surveil-
lance state” at siliconchip.com.au/link/aaoa
Facial recognition at concerts
In April 2018, there was a concert of 60,000 people in Chi-
na. A wanted person was identified among the vast crowd
by facial recognition technology and arrested by authorities
for “economic crimes”. The suspect was apparently extreme-
ly surprised that he could be identified and pulled out of a
crowd of so many people.
Fig.15: PrivacyFilter is another system to modify images,
preventing them from being used for face recognition.
It was developed by Joey Bose, an engineering student
at the University of Toronto. This system has now been
developed into a commercial product, “faceshield” (https://
faceshield.ai/)SCLetting a neural network decide what features are
important in a face
A YouTube user by the name of “CodeParade” took 1700 faces
and used a neural network program of his own design to encode
information from those faces. Without human decision making,
the program automatically decided what facial features were most
important and assigned them a level of importance.
A number of adjustable slider bars were generated which were
ranked by the program in order of importance, and these could be
adjusted to discover what facial features they corresponded to.
It was not always obvious what facial feature(s) the neural network
had selected. When the sliders were adjusted, the faces sometimes
changed in unusual ways and the changes were dependent upon
the position of the other sliders. See the video titled “Video Com-
puter Generates Human Faces” at siliconchip.com.au/link/aaoc