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So my work
entails, a modern state of the art, facial recognition
the work actually started just after 9/11.
There was quite a lot of work to be done in terms of biometrics
person recognition.
So I started that during that period, but it was kind of very rudimentary
sort of image processing type work for facial recognition.
And then more recently, over the last ten years or so
we have actually come very far in terms of facial recognition and generally object
recognition using machine learning and artificial intelligence.
So the way our facial recognition models works is that we
train, using millions and millions of faces.
So essentially it's a black box.
You can imagine it's a machine learning algorithm, an AI algorithm
to which we feed millions and millions of images of facial images
and suddenly it actually understands what a specific human face looks like.
Part of our facial recognition model also includes face aging.
Again, what we have done is we have taken the huge database
of human population; different ethnicities, gender
and we have fed this to a machine learning algorithm
and the machine learning algorithm figured out how humans age.
So we could potentially take your face and we can predict
what you would look like in 10 years, 15 years.
We can also go back and we can find out what you would have looked like
10 years, 15 years, 20 years back.
So because the model recognises different parts of the face
you don't actually need a full image of the face.
Exactly.
So, for example, you might have a situation
where you have thousands and thousands of images in a database
and then you have a very specific part of the face,
say the eye or the nose, in which case we can still do recognition.
Presumably that has some really interesting applications in things
like missing person investigations or criminal investigations.
Indeed. Indeed.
We all know about the the Schiphol poisoning in 2018, they had essentially
two suspects where they had images of these two suspects.
So they wanted to know whether these two images
are the same person for example.
We tried to bring them to the same age and then we did facial recognition
and we were able to identify successfully that
the images provided were from the same individual.
So once you managed to identify these these two gentlemen what happened next.
In this particular case
they've actually put together a report which they presented at the parliament.
That's a great example of how you can support
investigative journalism if you've got any other examples?
Yeah we've been working with New York Times, for example, for a number of years
where we have helped actually with their investigative
journalism as well on face recognition on several fronts.
I believe you work with the BBC on documentary
can you tell me more about that?
So it's a documentary called The Nazi Next Door.
We were able to identify a Nazi from a 70 year
old photograph who happened to be given immunity by the British Intelligence
and who happened to be living in this country.
This kind of work that we are doing actually helps
the public become more aware of problems like this
and also they can see how this kind of work actually benefits
the society in general.
Your research has been hugely impactful in a whole range of areas,
what does that mean for you and how are you going to take it forward?
I'm an academic and I'm a researcher, so I'm interested in solving
big problems, and I'm interested in actually utilising
face recognition to solve such big problems.
There are so many ways that we can take this research forward.
For example, identical twins - identification for identical twins
and many other areas.
So I'm really excited to push the field forward.