I'm Harriet Smith and I'm an Independent Research Fellow at Nottingham Trent University.
I'm going to talk about some research that we've been doing using interactive faces to
try and improve face matching performance for both superior and typical recognisers.
Recognising familiar faces is very easy, but trying to decide if two facial images feature the same unfamiliar person,
someone you don't know, can actually be quite difficult. Error rates in unfamiliar face matching commonly vary between 10 and 30 percent,
which is quite worrying when you consider that important security decisions might rely
on accurate performance. And even passport officers commit relatively frequent errors.
In this research, we tested a novel interactive face matching procedure, and we focussed on trying to improve performance. To explain our rationale,
first let's consider why unfamiliar face matching might be difficult.
So while it's easy for me to see that both these images feature my brother, you might find it more difficult because you don't know him.
Facial images can look different because they feature different people or because the same person looks different across poses,
orientations or lighting conditions. And when making matching decisions
you have to decide whether to attribute differences to within or between person variability.
And this isn't necessarily straightforward. So how might we try and improve unfamiliar face matching. Well,
perhaps showing the face moving fluidly from side to side might help people to build a 3D view-
independent representation, thus providing knowledge of how the face would look from different orientations.
Now this is the kind of knowledge that we already have when we know a person, but that we lack when we don't know a person.
Interactivity might also help boost performance. So that is being able to manoeuvre a facial image to any viewpoint.
And this would mean that the facial image could be viewed in the same position as a comparison image.
So the user would be able to reduce differences between two images and that would make the matching decision easier.
Now although unfamiliar face matching can be challenging,
some people are actually extremely good at remembering and perceiving faces and we refer to them in psychology as super recognisers.
So we tested super and typical recognisers separately just in case they perform differently when using our novel interactive face matching procedure.
Although super recognises only represent around two percent of the population,
some police have police forces now deploy super recognisers to ID critical roles.
So it's important that we know how both groups perform when using this new procedure.
So in a series of four experiments we tested our novel, interactive matching procedure.
We tested people with typical face recognition skills in Experiment One and those with superior face recognition skills in Experiment Two.
And then we directly compared them in Experiment Three and Four. So we've got quite a lot of results.
There's a lot to cover here. So I'm only going to very briefly summarise our key findings.
And then at the end of the presentation, I'll provide a link to our paper in case you'd like any further information.
Participants in each of the four experiments were recruited from an online database which is managed by our collaborator, Professor Josh Davis.
We invited people who'd previously completed a diagnostic face memory test and whose scores were within the typical range.
The face matching tests for each experiment were constructed from a database featuring Facebook
profile images for each individual and a video of their face moving from side to side.
Each participant had differing amounts of orientation information available to them, and some had the ability to interact with facial images.
Testing and comparing four different conditions allowed us to thoroughly test the interactive procedure.
So now I'll show you how each of the conditions appeared to participants. In the frontal condition -
this is the standard procedure.
So participants have two static frontal images and they have to decide whether the images feature the same person or different people.
In each condition, participants completed 94 trials and in half the trials, the correct response would have been to respond to 'same identity'.
And then the other half of the trials, the correct response would have been to respond 'different identity'. In the orientations condition
the only difference is that now participants have some orientation information.
Unlike in the moving condition, they don't have any fluid movement.
And this is the novel interactive procedure, which combines orientation information,
fluid movement and interactivity so the user can click and drag the face to any orientation and hold it there for as long as they want.
These are the results from experiment one, and this plot shows accuracy in each of the four conditions.
You can see the conditions labelled on the x axis:
frontal, orientations, moving and interactive. Performance when the correct response would be to respond
'same' is shown by the black dots. And performance from the correct response would be to respond
'different' is shown by the grey dots.
And the results suggest that typical recognisers benefit from orientation information when matching unfamiliar faces.
You can see overall accuracy creeping up from left to right. And in the interactive condition,
typical recognises were more likely to respond that images featured different people.
Now, this is crucial. So it seems that interactivity increases the salience of differences between facial images.
And it's crucial because when we consider that in an identity verification context,
it's accurate 'different identity' decisions that help to prevent identity fraud.
In Experiment Two the procedure was exactly the same as Experiment One, apart from this time
we tested superior face recognisers, so we recruited people who had scored highly on the diagnostic face memory test.
And as expected, accuracy was very high because these are superior face recognisers. And you can see that unlike the typical
recognisers, superior recognises are very good at spotting when images feature different people.
So different identity accuracy is consistently high across all four conditions.
Now here, the moving and interactive condition appear to support superior recogniser performance in that
the difference between accuracy on same identity and different identity trials is reduced.
Now let's look at the results from Experiment One and Two together.
It's clear that the interactive procedure has the potential to shift patterns of performance in comparison
to the standard frontal condition, which is currently used in practise, and that it supports accuracy.
Notably the interactive procedure makes typical recognises behave in a similar way to superior face recognisers.
So it makes them both more accurate overall and more likely to respond that two images feature different people.
In Experiment three and four, we directly compared typical and superior face recognises.
And here we only compared performance across the frontal procedure, or the
procedure that's currently used in practise, to our novel interactive procedure,
because these are the two conditions that we're most interested in. Here
you can see the results from Experiment Three. So across both groups, accuracy was higher in the interactive condition than in the frontal condition.
So the results confirm what we previously found:
both typical and superior recognisers benefit from interactivity when matching unfamiliar faces. As in Experiment
One we see that the typical recognisers the interactive procedure particularly boosts performance when images feature different people.
As I've already explained, these decisions are crucial for preventing identity fraud.
So far, accuracy has been very high in all of the three experiments that I've explained so far.
But in Experiment Four the testing conditions reflect potential challenges in forensic context.
So situations where users might need to match low quality images.
So, for example, images from CCTV. Again,
we're comparing groups of typical and superior recognisers across standard and interactive face matching procedures.
Unlike in Experiment Three, we now pixelate one of the images to make the task harder.
As expected, overall accuracy was lower than in previous experiments. As in Experiment
Three overall accuracy is higher in the interactive condition for both groups.
So the interactive procedure also boosts performance when the task is more difficult.
So now I'm going to explain some of the conclusions that we can draw based on these four experiments.
Now I've only provided a very brief summary of our findings.
So please do you scan this QR code to access our paper, which was recently published in the British Journal of Psychology.
Our work shows that across the spectrum of recogniser ability and across different image qualities,
our interactive procedure improves face matching performance. As I've already explained, some police forces already deploy super recognisers. So
crucially, our work shows that the risk of incorrect face matching decisions can be minimised even
further by combining the use of super recognisers with using an interactive procedure.
This innovation is low cost and it's easy to integrate into existing police systems.
Taken together, these findings have important forensic and security implications for identity verification contexts.
So please don't hesitate to get in touch if you'd like to discuss any aspect of this research.
Thank you for listening.