1. Keesing Journal of Documents & Identity February 2015 3
From policing to passport control
The limitations of photo ID
by David Robertson, Russ Middleton and A. Mike Burton
Identity verification at passport control, in policing, and in retail stores is most often achieved by
matching an individual’s face to a photographic identity document. Despite this, recent research
has shown that unfamiliar face recognition is a difficult task, and one which is highly prone to error.
In this article, David Robertson, Russ Middleton and Mike Burton outline evidence which establishes
the difficulty faced by professionals in occupations which require accurate face recognition, and
suggest new avenues of research which may reduce current levels of human error and have the
potential to improve real‑world recognition performance.
Face recognition
National security officials, officers in the criminal justice
system and retail staff frequently rely on face recognition
to establish and authenticate the identity of an
individual. At UK Border Control, officials work to ensure
that only those passengers whose passport photo
matches their face are allowed to enter the country. In
the criminal justice system, police officers often utilise
CCTV images as a means of identifying the perpetrator
of a crime. In addition, cashiers in retail stores must
examine face‑photo ID cards in order to prohibit the
illegal sale of age‑restricted goods. Each of these
occupations relies on the ability to detect correctly
whether or not the face of an unfamiliar person matches
a face photo on an ID card or an image still.
Our reliance on face recognition for identity verification
may stem from the fact that in some instances we show
a high level of expertise in this area. For example, we
are able to recognise familiar faces across a large
range of highly variable photos, apparently effortlessly.
However, in a striking contrast, recent research has
shown that we are surprisingly poor at recognising
new instances of an unfamiliar person. This distinction
has major implications for applied professions in
which accurate unfamiliar face recognition is vital.
Unfamiliar face recognition:
laboratory studies
Our ability to recognise familiar faces is certainly
impressive. Figure 1 shows a considerable range of
visual conditions, such as changes in the viewing angle,
physical appearance, lighting and camera. Nevertheless,
familiar viewers find it easy to recognise this person.
However, recent research has shown that this ability
does not generalise to the identification of similar
instances of unfamiliar people. For example, Burton et
al developed the Glasgow Face Matching Test (GFMT),
a psychometric test of unfamiliar face recognition
ability, in which participants are required to decide
whether a pair of face photos depicts two instances of
the same person (taken seconds apart using different
cameras) or two different people (see Figure 2).1
This type of one‑to‑one matching task is the
experimental analogue of the paired comparisons
made by passport officers (face‑passport photo) and
cashiers (face‑photo ID) on a daily basis. Despite the
GFMT using high‑quality front‑facing images, error
rates of between 15% and 20% are the norm, across
hundreds of viewers tested.
Figure 1 (top)
Ambient photos of the same face. All images used under
Creative Commons or Open Government Licence.
Practical experience
The limitations of photo ID
David Robertson
University of York, UK, is a
post-doctoral researcher
in Mike Burton’s face
research group. His work
focuses on assessing
and improving unfamiliar
face recognition in
professional contexts
(www.facevar.com).
Email: david.robertson@
york.ac.uk.
Russ Middleton is an
expert in identity fraud
with 30 years’ experience
working as a detective in
the Metropolitan Police.
He is currently a director
of Delmont-ID, an anti-
fraud training and
consultancy firm.
A.Mike Burton, University
of York, UK, is a Professor
of Psychology and a
specialist in face
recognition research. His
current work, supported
by major grants from the
ERC and ESRC, is focused
on improving our
understanding of face
recognition in both
theoretical and applied
contexts. Email:
mike.burton@york.ac.uk.
Figure 2 (left)
Example of two trials from the Glasgow Face
Matching Test (GFMT). The left pair shows
two instances of the same person (match
trial), the right pair shows two different
people (mismatch trial).

2. Keesing Journal of Documents & Identity February 20154
In contrast to the one‑to‑one matching tested by the
GFMT, Bruce et al had previously developed a task which
modelled an old‑fashioned police line‑up scenario
which involved a series of one‑to‑ten matching arrays.2
As seen in Figure 3, participants were presented with a
single high‑quality front‑facing image still of a ‘suspect’
(taken from high‑quality video footage), below which
was presented an array of ten face photos. Participants
were required to decide whether or not the suspect
was present in the array, and if so, to pick him out.
Error rates on this task were very high, 30% on average,
despite the photos being taken on the same day, in
very similar pose and in optimal lighting conditions.
Of course, with lower quality images, performance
drops still further (see Henderson et al, 20013
), but
the key point is that it remains far from perfect, even
with the highest quality images. In short, it is not the
technology which limits performance on unfamiliar
face matching; it is the properties of human vision.
All these studies examine people’s ability to match
photos. However, it turns out that matching a photo to
a live person is no easier. For example, Megreya &
Burton showed that both one‑to‑one and one‑to‑ten
matching was no easier for unfamiliar viewers, even
when the target person was standing in front of the
viewers.4
Davis & Valentine showed that people were
very highly error‑prone when trying to match a live
person to a full CCTV clip of the person, taken a short
while earlier.5
Although these lab studies are informative (and have
been used to inform theories of the perceptual
processes involved in face recognition), they are all
performed on non‑specialist viewers, typically
students. However, in real settings, it is important to
know whether people who carry out these tasks
professionally are able to perform more accurately than
untrained viewers. We come to these studies next.
Unfamiliar face recognition:
studies on specialist face recognisers
Retail staff
Kemp et al provided one of the first real‑world
demonstrations of unfamiliar face recognition
performance.6
The study aimed to assess whether
identity fraud could be reduced by including a face
photo on one’s credit card. Supermarket cashiers were
required to decide whether or not the face photo on a
credit card matched the live face of the customer
standing in front of them (half held genuine cards, half
held fraudulent cards). The cashiers were aware of the
purpose of the study and knew that their performance
was being observed and recorded. Despite this, Kemp
et al reported that fraudulent cards – cards which
contained a photo of a person different to that of the
bearer – were accepted as genuine on 50% of
occasions. The authors concluded that the
introduction of photo ID credit cards would do very
little to improve the detection of fraud at the point of
sale. More broadly, as mentioned above, cashiers are
routinely required to check photo‑ID before selling
cigarettes or alcohol. It is reasonable to assume that
the level of error reported by Kemp et al would be
similar for photo ID cards in this context.
Police officers
Burton et al compared the performance of a group of
university students and a group of 20 police officers
with experience in forensic identification (13.5 years of
service on average).7
As illustrated in Figure 4,
participants were required to view low‑quality video
Figure 3
Example of a 1-10 matching
trial from Bruce et al (1999).
Is the ’suspect’ one of the 10
people in the line-up, and if
so which one? Answer at end.
1 2 3 4 5 6 7 8 9 10

3. Keesing Journal of Documents & Identity February 2015 5
100
90
80
70
60
50
0 5 10 15 20 25
Facematchingaccuracy
(percentcorrect)
Employment duration (years)
clips of individuals entering a building, whom, they
were told, they would later be asked to identify. The
participants were then shown high‑quality face photos
(see Figure 4) and asked to rate how confident they
were that these individuals had been present in the
video clips. Police officers showed very poor accuracy
on this task, and in fact did no better than a group of
students. Both groups were almost at chance, leading
one to ask whether the videos were of such poor quality
that the task was impossible. However, Burton et al
also performed the same test on viewers who knew
the people depicted in these videos. This group was
almost perfect in their accuracy. Once again, we see a
huge advantage for familiar over unfamiliar viewers –
and a clear indication that, for this task, police officers
were no better than any other unfamiliar viewer.
Passport officials
A recent study of unfamiliar face recognition in
30 Australian passport officers was conducted in
collaboration with the Australian Department of
Foreign Affairs. White et al asked the passport officers
to decide whether a passport photo matched the face
of a person standing in front of them.8
The study
reported that the passport officials incorrectly accepted
a fake passport photo as genuine on 14% of trials.
Moreover, as shown in Figure 5, there was no
relationship between employment duration/
experience and accuracy on this task. In short, those
who had 20 years’ experience, were no more likely to
be accurate than new recruits. As the figure shows,
there was very wide variation between officers. In fact,
this is the standard finding – some people are better
at these face tasks than others. However, it does not
seem to be the case that professional training
necessarily leads to high performance levels. To put
this finding into perspective, some of the world’s
busiest airports handle over 200,000 people every
day. It is clear that an average error rate of 14%
corresponds to several thousand ID errors a day – an
unacceptable security risk.
Automatic face recognition: airport e‑Gates
Although we have concentrated on human face
recognition in this article, recent advances in
technology have led to the installation of electronic
facial recognition gates (e‑Gates) at UK airports. These
machines scan a passenger’s face and attempt to
match it to their passport photo. While in theory this
should remove the level of human error reported above,
these machines have not proven to be the security
panacea that many assumed. Indeed, a recent UK
Inspectorate of Borders report queried whether the
e‑Gates were providing adequate security when, for
example, a husband and wife were able to accidently
swap passports and still make it through the system.9
Despite the claims of suppliers, automatic recognition
systems have not yet reached the levels of accuracy
necessary to make them practical to use at airports.10‑11
How to improve face photo identification
The preceding sections have shown that unfamiliar
face recognition is a difficult task which is highly prone
to error; regardless of whether one uses a live face or
face photos, and whether one has a human or
machine recognition system. Despite these findings,
we maintain our reliance on photo‑ID in security,
policing and retail contexts. If we are to persevere with
this form of identity verification we must seek ways to
improve human and machine performance. Recent
advances in research, which we outline below, have
begun to investigate ways in which unfamiliar face
recognition can be improved.
Selection
The findings from the White et al passport office study
showed that performance on the recognition task was
not related to experience/years in employment.8
This
suggests that some people are naturally good at this
type of task, as can be seen in Figure 5. In future, an
established psychometric test of face matching ability
Practical experience
The limitations of photo ID
Figure 4
Low‑quality versus high‑
quality photos. Examples
images from Burton et al
(1999). A still from the CCTV
video (left) and a high-
quality photo (right).
Figure 5
Unfamiliar face recognition accuracy (live face to passport photo) for Australian passport office
staff, as a function of employment duration (White et al, 2014).

4. Keesing Journal of Documents & Identity February 2015 7
Practical experience
The limitations of photo ID
work together and come to a judgement in pairs.13
Across four experiments, the study tested unfamiliar
face recognition individually (pre‑test), as pairs
(paired‑test) and again individually (post‑tests).
The authors report that both low‑performing and
high‑performing participants were found to be more
accurate when they made their judgements in pairs
than in the individual pre‑test phase. Furthermore,
those who started with low performance showed a
lasting benefit of having worked in pairs, suggesting
that this type of procedure may be a particularly
effective training method.
Multiphoto ID
One final method of improving unfamiliar face matching
focuses on the ID document rather than the selection
and training of the human recogniser. One often hears
the phrase ‘your passport photo looks nothing like
you’ and it seems clear that a single instance of a
person can never form a true representation of their
appearance. Our research suggests that the key to
improving unfamiliar face recognition is learning how
an individual varies across a naturally occurring set of
instances. In other words, ‘familiarity’ is short‑hand
for learning an individual’s idiosyncratic variation in
appearance (see Burton, 201314
). One method of
achieving this for photo‑ID is to increase the number
of photos of the bearer on the document. White et al
reported that unfamiliar face matching performance
significantly improved when mock ID cards contained
2, 3 or 4 photo arrays (see Figure 6).15
These findings
suggest that a relatively small increase in the number
of photos contained on an ID card could reduce the
error found with single image identity documents (see
Jenkins & Burton for the potential for ‘face averages’
to improve automatic face recognition systems16).
such as the GFMT could be used to assess and select
the best candidates for positions in which accurate
face recognition is vital.
Feedback training
A further study by White et al showed that performance
on the GFMT (short version) was improved by 10%
when trial‑by‑trial feedback was provided (i.e. the
participant was informed whether they had made the
correct judgment after each response).12
However, the
most interesting finding was that the GFMT feedback
training also led to a performance enhancement on an
entirely novel set of naturally varying images. This is
the first such evidence that training performance on
one set of images can lead to a generalizable
improvement in unfamiliar face recognition.
Paired decision making
Dowsett & Burton have shown that performance on
recognition tests can be improved when participants
Example of the face matching arrays from White et al (2014). A single face is presented to the left, the array of photos (1, 2, 3 or 4
photo-arrays) to the right. Participants decide if the face on the left matches the photo(s) on the right. Example arrays showing 1
and 3 photos show the same person (match trials), while arrays showing 2 and 4 photos show different people (mismatch trials).
Figure 6
Mock ID cards containing
more than one photo.

5. Keesing Journal of Documents & Identity February 20158
Practical experience
The limitations of photo ID
Conclusion
Unfamiliar face recognition is a difficult task which is
highly prone to error; regardless of whether one uses a
live face or face photos or whether one has a human
or machine recognition system. Given the importance
placed on identity verification from photo‑ID in a variety
of important contexts, we must seek to find new ways
to eliminate human error if we are to improve security
and cut fraud.
Answer to line-up in Figure 3: suspect is not present.
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