Publication - The Glasgow Face Matching Test

Behavior Research Methods
2010, 42 (1), 286-291
doi:10.3758/BRM.42.1.286

The Glasgow Face Matching Test
A. Mike Burton and David White

University of Glasgow, Glasgow, Scotland
and

Allan McNeill

Glasgow Caledonian University, Glasgow, Scotland
We describe a new test for unfamiliar face matching, the Glasgow Face Matching Test (GFMT). Viewers are
shown pairs of faces, photographed in full-face view but with different cameras, and are asked to make same/­
different judgments. The full version of the test comprises 168 face pairs, and we also describe a shortened
version with 40 pairs. We provide normative data for these tests derived from large subject samples. We also
describe associations between the GFMT and other tests of matching and memory. The new test correlates
moderately with face memory but more strongly with object matching, a result that is consistent with previous
research highlighting a link between object and face matching, specific to unfamiliar faces. The test is available
free for scientific use.

Traditional research on face perception has tended to
focus on two aspects of the problem: recognition of familiar faces and memory for unfamiliar faces. Theoretical
models, such as that offered by Bruce and Young (1986),
have been used for understanding familiar face recognition
in typical observers and neuropsychologically impaired
patients. Research on face memory, on the other hand, has
tended to be led by difficult forensic problems, such as
eyewitness testimony (e.g., Lane & Meissner, 2008; Malpass & Devine, 1981; Searcy, Bartlett, & Memon, 1999;
Wells & Olson, 2003).
In recent years, it has become clear that unfamiliar face
matching is a problem worthy of study in its own right. At
first glance, this might appear to be a simple problem, but
recent research has shown that matching unfamiliar faces
is, in fact, rather difficult, even when high-quality images
are used. Bruce et al. (1999) presented viewers with 1-in10 arrays, in which a photo of a young man was accompanied by 10 possible matches. All the images were shown
in a very similar pose (full face) and in good lighting and
had been taken on the same day, eliminating transient differences due to hairstyle, weight, and so forth. Crucially,
target and array photos were taken with different cameras
(one a high-quality video camera and one a studio film
camera). Under these seemingly optimal conditions, with
no time constraints, and with instructions emphasizing accuracy, viewers performed surprisingly poorly. They were
accurate only 70% of the time, for both target-present and
target-absent arrays. This basic finding has been replicated many times and has been extended to situations in
which only target-present arrays were shown, reducing the
problem to a 1-in-10 forced choice, and in which viewers

scored only 80% accurate (Bruce, Henderson, Newman,
& Burton, 2001). These accuracy rates have also been replicated using an entirely different stimulus set, Egyptian
young men as targets, with Egyptian students as viewers
(Megreya & Burton, 2008).
In subsequent studies, researchers have used simple
pairs of faces to measure matching ability (Clutterbuck &
Johnston, 2002; Megreya & Burton, 2006, 2007). Under
these circumstances, similarly poor matching rates have
been observed. Typically, people have found it surprisingly difficult to match two images of an unfamiliar person, making between 10% and 25% errors, depending on
the particular stimulus sets that were used. These error
rates have never been experienced in matching familiar
faces, where ceiling levels of performance have been observed (see Hancock, Bruce, & Burton, 2000). Indeed, a
series of experiments by Clutterbuck and Johnston (2002,
2004, 2005) showed that the ability to match images of
faces was a very good indicator of the viewer’s level of
familiarity with a face and improved predictably with increased exposure to the person depicted.
All the studies listed above employed photo-to-photo
matching, rather than live-person-to-photo matching.
There are a number of security-related situations in which
photo-to-photo matching is important—for example,
when one tries to match an image of a suspect to a surveillance camera image from a crime scene. However, it
is also becoming increasingly common to ask viewers to
match photos to live faces. Matching a photo to a face is
required not only for passport control, but also in more
commonplace settings, such as verifying one’s age in
order to buy alcohol. Two studies have recently demon-

A. M. Burton, [email protected]

© 2010 The Psychonomic Society, Inc.

286

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Glasgow Face Matching Test     287
(Benton, Hamsher, Varney, & Spreen, 1983). This test requires participants to match faces across different views.
However (and crucially), all images are taken with the
same camera. The test we present here tackles a different
problem: matching two images in the same view but taken
with different cameras. No existing test of face processing
incorporates this task, perhaps because it has only relatively recently become clear that it is nontrivial. Moreover, the issue of camera change is an important one in
forensic settings and in everyday verification of photo ID.
We have argued that it introduces important variability
that discriminates familiar from unfamiliar face processing (Burton, Jenkins, Hancock, & White, 2005; Jenkins
& Burton, 2008).
To summarize, the test of face matching described in
the remainder of this article is intended to complement
existing tests of face processing, rather than to replace
any existing tests. It measures performance on a task that
is not trivially easy and has been shown to correlate well
with levels of familiarity. Furthermore, it mimics a situation that is commonly encountered in security settings:
how to match two unfamiliar face images in similar poses
but taken with different cameras.

A

B

Figure 1. Example test items from the Glasgow Face Matching
Test. (A) Mismatching pair. (B) Matching pair.

strated that matching a live person to a photo is no easier
than matching two photos of the same person (Davis &
Valentine, 2009; Megreya & Burton, 2008). This suggests
that the psychological study of face matching addresses a
problem of practical, as well as theoretical, consequence.
A test for face matching

Test Score (% Correct)

There are a number of tests of face recognition ability
already available. However, many of these measure face
memory rather than matching—for example, the Recognition Memory Test for faces (Warrington, 1984) and the
Cambridge Face Memory Test (Duchaine & Nakayama,
2006). Of the available instruments for measuring matching ability, the Benton test is the most commonly used

Test Construction
To build a new database of faces, volunteers were recruited through advertising posters in student recreation
areas of a university. Three hundred four individuals contributed their time in exchange for a small payment. They
were 172 men and 132 women, with the mean age for men
being 22.9 years (SD 5 6.7), and for women 23.2 years
(SD 5 7.0). Over the course of a single session, each
volunteer was photographed in a variety of poses, using
two different digital cameras. Volunteers were also filmed
moving between poses and expressions, using a digital
video camera. Thus, for each volunteer, we have images
from three different capture devices taken on the same
day. This large database continues to expand with new volunteers and is available from the authors on request (see
the Note for details).
The Glasgow Face Matching Test (GFMT) comprises
168 pairs of faces. For the construction of the test, only

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Figure 2. Cumulative frequency of accuracies for the Glasgow Face Matching Test.

288     Burton, White, and McNeill

Figure 3. Example array from the visual short-term memory
test.

full-face poses were used, in which volunteers displayed a
neutral expression. For each person, we used the full-face
image from one of the still cameras (Camera 1: Fujifilm
FinePix 0800Zoom, 6 megapixel) and a frame in the same
pose taken from the video camera (Camera 2: Panasonic
NV-DS29B DS29). All images were captured against a
background screen, from a distance of 90 cm. The fixed
sequence of the photographic session ensured that these
two images were taken roughly 15 min apart.
Following image capture, all the photos were edited to
remove the background and any visible clothing. Images
were cropped neatly around the head, using graphical software, and were resized to 350 pixels width, before being
stored in grayscale at a resolution of 72 ppi. When pairs
of stimuli were constructed for the test, faces were positioned in such a way that the horizontal distance between
the bridge of the nose in the two images was 500 pixels.
Of the 168 test pairs, half are same-face trials, in which
two images of the same person are presented side by side.
These 84 people are also used in different-face trials, such
that one of the person’s images is presented alongside a
similar face from the database. The nonmatching faces
for these trials were chosen on the basis of a pilot study in
which pairwise similarity measures were generated using
a sorting technique (see Bruce et al., 1999). The foils for
these trials were the faces most similar to each of the target identities. For different trials, as with same trials, the
two photos always came from different cameras. Figure 1
shows examples of face pairs.
Performance on the Test
Subjects. Following initial pilots, the GFMT was
presented to 300 subjects. This was a relatively heterogeneous sample, recruited through advertisements in the
local media. There were 120 males and 180 females. Mean
age was 30.8 years, with a range of 18–80 and a standard
deviation of 14.

Performance. Overall accuracy ranged from 62%–
100%, with a mean of 89.9% (SD 5 7.3). Performance
was slightly better on matching items (92%) than on mismatching items (88%), indicating a small response bias to
respond same. Couched as detection measures, this gives
a d ′ value of 2.91, with a criterion of 20.09. With this
large sample size, criterion is significantly below zero
[t(299) 5 4.69, p , .01]. There was no correlation between accuracy and age of viewer (r 5 .09),1 and there
was no performance difference between men and women
[male 89%, female 90.4%; t(298) 5 1.53, n.s.]. In order
to measure the internal reliability of the test, we examined
the split-half association by correlating the subjects’ performance on the first and second halves of the test items.
Association was high, with r 5 .81.
Figure 2 gives the cumulative distribution of accuracies
and may easily be used to establish the norm of any score
against this population. As one might predict for a test
of this kind, the distribution is negatively skewed (skewness 5 21.33, p , .05). However, it is interesting to note
that performance is far from perfect. Recall that the test requires the observer to match two photos of a person taken
minutes apart, in the same pose, with two high-­quality
cameras. If we consider that the median performance is
92%, this means that half the sample make at least 8%
errors—that is, 13 items wrong across the 168 items in the
test. Similarly, the poorest 25% made at least 24 matching
errors. In a test with no time limits, in which accuracy is
emphasized, this is perhaps surprising, although it is consistent with our previous work showing rather poor levels
of performance on unfamiliar face matching.
Finally, we note that the mean time to complete the selfpaced test was 15 min and that there was a small, but reliable, positive correlation between overall accuracy and
time taken (r 5 .177, p , .01).
Association between the GFMT
and other tests of face
and object processing
The matching test described above reveals substantial individual differences in a task that, at first glance,
might appear relatively easy. In order to establish whether
this variation reflects more general variation in visual­processing abilities, we also examined our subjects’ performance on three more commonly used tests of visual
matching and memory. Each of the 300 subjects who took
part in the study above also contributed measures on three
further tests: (1) recognition memory for faces, (2) the
Matching Familiar Figures Test (MFFT), and (3) a visual
short-term memory test.
Table 1
Performance on Four Tests of Matching and Memory

Mean (% correct)
SD

GFMT
89.9
  7.3

Recognition
Memory
for Faces
62.4
10.0

Matching
Familiar
Figures
66.3
21.9

Visual
Short-Term
Memory
62.9
  9.4

Glasgow Face Matching Test     289
Table 2
Correlations Between Tests: Pearson’s r
Recognition
Matching
Memory
Familiar
Visual
for Faces
Figures
STM
Age
GFMT
.285**
.420**
.050
.090
Recognition memory for faces
–
.158*
.186*
2.209**
Matching Familiar Figures Test
–
–
.176*
2.023
Visual STM
–
–
–
2.177*
Note—STM, short-term memory; GFMT, Glasgow Face Matching Test.
*p , .01.  **p , .001.

Test Score (% Correct)

1. Recognition memory for faces. For this test, a further 40 people’s faces from the same database were used
(20 men and 20 women). Images were prepared in exactly
the same way as described above, were presented to the
subjects in grayscale, at the same size and resolution as
those in the GFMT, and were cropped of background in
the same way.
To test recognition memory, the subjects were shown
images of 20 of the faces, all taken with Camera 1. The
subjects sat in front of a computer screen and were instructed to pay close attention to the faces, since they
would be asked to identify them later. The images appeared in sequence for 2 sec each, preceded by a fixation cross for 750 msec. Once all 20 images had been
presented, a message appeared instructing the subjects to
wait for further instructions. After a 20-sec interval, test
phase instructions appeared. During test, the viewers were
presented with 40 faces, all taken with Camera 2 (i.e., not
the same camera as that used for images in the first phase).
They were told that they should decide, independently for
each face, whether it had appeared in the earlier phase.
Testing was self-paced.
2. Matching Familiar Figures Test. The MFFT is a common technique for measuring cognitive style, impulsivity
versus reflexivity (Kagan, 1965). The test consists of 20
standard line drawings of common objects (targets) and
six variants of each object, one of which is identical to the
target image. Performance on this test has been shown to
correlate with performance on unfamiliar-face-matching

tests in previous research using a lineup task (Megreya &
Burton, 2006).
3. Visual short-term memory for objects test. For this
test, circular visual arrays of objects were constructed.
Forty-five common objects were taken from the database
of Rossion and Pourtois (2004). These were used to create
six circular arrays of 5, 6, 7, 8, 9, and 10 objects. An example is given in Figure 3. Testing followed the procedure
described by Miller (1956), in his highly influential account of memory span. The subjects were presented with
each array in turn, starting with the array with the fewest
objects (5 items) and ending with the array with the most
objects (10 items). Each array was presented on the screen
for 5 sec, after which the subjects were asked to write as
many of the items as they could remember on a sheet of
paper provided to them.
Results and Discussion
Table 1 shows the overall performance levels for the
GFMT and the three tests described here. Table 2 shows
the association between the tests (Pearson’s r), as well as
the correlation between performance on the test and the
subjects’ ages.
There are a number of points to note from these data.
First, the highest correlation with the GFMT is the MFFT.
This is consistent with the notion that unfamiliar faces
tend to be processed as general visual objects, without
recruiting the perceptual processes that lead to very robust performance with familiar faces (e.g., Hancock et al.,

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Figure 4. Cumulative frequency of accuracies for the short version of the Glasgow
Face Matching Test.

290     Burton, White, and McNeill
2000; Megreya & Burton, 2006). Note that the high association between the GFMT and MFFT occurs despite
some large differences in the format of the tests. Notably,
the GFMT involves a yes/no response to pairs of faces,
whereas the MFFT involves a lineup of six options. Furthermore, the MFFT contains only target-present items; a
match always exists. Nevertheless, there is a striking association here.
There is a smaller association between face matching
and face memory, using these tests. Nevertheless, there is
a substantial effect here, suggesting some shared processing. Note that the recognition memory test for unfamiliar
faces is very difficult (M 5 62%, with chance being 50%),
in contrast to many similar tests in the literature that use
the identical image at learning and at test. This inevitably
skews the memory data positively and, therefore, may lead
to an underestimation of the correlations with other measures. Nevertheless, it is noticeable that this is the only
measure that correlates with all the other tests. Perhaps
more interesting is the pattern of associations between the
tests and the subjects’ ages. It is clear that both tests of
memory show a decline in performance with age. This is
the case despite large differences in style between the two
tests of memory (faces or objects, delayed vs. immediate
memory). However, the association with age is completely
absent in the two rather different tests of matching. This
observation appears interesting and will be followed up in
future research.
A short version of the GFMT
The full GFMT comprises 168 pairs of faces and is
self-paced. We anticipated that some users would prefer
a briefer test, and so we developed a shortened version
comprising only 40 face pairs. Items for this test were
selected as being the most difficult items from the full
version. Using data from the test of 300 subjects above,
the 20 matching and 20 nonmatching items were chosen
that had resulted in the most errors. Scores on this subset
of items correlated very highly with overall scores on the
full test (r 5 .91), making this a potentially useful version
of the test.
The short version of the GFMT was tested on 194 new
volunteers, none of whom had taken part in the studies
described above. These were young adult subjects with a
mean age of 26 years (range, 18–46). There were 121 men
and 73 women. The test was run self-paced and typically
took between 3 and 4 min to complete, making it appreciably shorter than the full version.
Mean performance on the short test was 81.3%, with
SD 5 9.7 and range 5 51%–100%. This is substantially
lower than performance on the full test, confirming the
choice of difficult items. Mean performance on match
and mismatch trials was 79.8% and 82.5%, respectively.
Figure 4 shows the cumulative distribution of accuracies
and may easily be used to establish the norm of any score
against this population. The test is significantly negatively
skewed (skewness 5 20.45, p , .05), although rather less
so than the full version.

General Discussion
We have presented a new test for face matching. Unlike other available tests, the GFMT presents two images
taken in the same pose, minutes apart, with high-quality
cameras. Despite these apparently optimal conditions, this
task is not trivially easy, and we have demonstrated that
there is large interindividual variation in performance.
We note that modern security measures mean that people are commonly asked to prove their identity with a
photograph. Correspondingly, there are very many people
whose daily activity requires them to confirm somebody’s
identity in this way. Previous research has established that
unfamiliar face matching is a surprisingly difficult task,
and we have recently demonstrated that matching a live
person to their photo is no easier than matching two photos (Megreya & Burton, 2008). With this in mind, we have
constructed a test that does not make the task artificially
difficult—for example, by covering people’s hair or requiring a match across different poses. Instead, we have
examined a commonplace match, two full-face views in
good lighting, in an attempt to mimic situations in which
one is trying to optimize the accuracy of a photo ID, not
to make it difficult.
Given the substantial individual differences in face
matching demonstrated here, we anticipate that one potential use of the test may be in personnel selection for
particular tasks requiring face matching. There is clearly
also a potential for use in training: Since almost no one we
tested showed perfect performance, it would be interesting
to use difficult items in training regimes. There is also a
clear potential for neuropsychological use of the test.
Author Note
This work was supported by Grant 000-23-1348 from the ESRC to
A.M.B. and A.M. The full GFMT and the short version are available for
download from the authors’ Web site at www.psy.gla.ac.uk/gfmt. The test
is free for research use, and the download package includes instructions,
scoring sheets, and the norm data presented here. All those who volunteered use of their faces for this test have provided written permission
for the images to be used for any research purposes, including scientific
publication. The full database of images (Glasgow Unfamiliar Face Database) from which the test was derived is available at the same site.
Correspondence concerning this article should be addressed to A. M.
Burton, Department of Psychology, University of Glasgow, Glasgow
G12 8QQ, Scotland (e-mail: [email protected]).
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1. Previous research (Searcy et al., 1999) suggests that adult age may
be more strongly associated with false positives than with hits. However,
that association was not present here: Correlations with age were r 5
.197 and 2.023 for hits and false positives, respectively.
(Manuscript received April 7, 2009;
revision accepted for publication May 24, 2009.)