Memory for Faces

Running Head: FACIAL RECOGNITION IN EYEWITNESS MEMORY 1
Facial Recognition: Eye Gaze, Confidence, and Accuracy of Eyewitness Memory for
Target Faces at Different Ages
Brandon Bogus
University of Nebraska – Lincoln

FACIAL RECOGNITION IN EYEWITNESS MEMORY 2
Abstract
The primary purpose of this study was to determine facial recognition accuracy in
eyewitness memory in terms of eye gaze and confidence ratings when altering the age of
lineup targets. Previous studies have focused on testing participants’ memory using
pictures of individuals where the pictures used in the encoding and recognition phase
were taken at roughly the same time. This sparks the question of how facial recognition
accuracy is affected when a significant amount of time passes between the encoding and
recognition phase. Forty-seven undergraduate psychology students from UNL
participated in the study in exchange for research credit. Investigators sought out subjects
who volunteered to view photos of individuals, complete a filler task, view more photos
of individuals while stating if a particular individual was previously seen and their
confidence level of identification, and answering demographic questions. Memory of a
target’s appearance is less accurate as the age gap increases between encoding and
recognition. Confidence is a weak predictor of memory accuracy. No relationships were
found in gaze time between external and internal features when shown an unfamiliar or a
familiar face or when shown a younger face followed by an older face of the same
person. Future research proposes a study that looks at the relationship between gaze time
on a particular facial feature and memory accuracy.

Facial Recognition: Eye Gaze, Confidence, and Accuracy of Eyewitness Memory for
Target Faces at Different Ages
In 2011, a Dallas man by the name of Cornelius Dupree was declared innocent of
the crime for which he served 30 years in prison. He was convicted of rape and robbery
of a young woman in 1979 and was sentenced to 75 years in prison after the female
victim identified Dupree as the perpetrator. After serving 30 years in prison, DNA
evidence revealed that he was not the offender (Green & Heilbrun, 2014). Unfortunately,
this is not the only case in which eyewitness misidentification lead to conviction of an
innocent person. Another alarming example is the People v. LeGrand (2007) case. In
1991, a cab driver was stabbed to death in Manhattan. Four people witnessed the event
and formed a composite sketch of the assailant. Two years later, the defendant was
identified as a possible suspect after being arrested for an unrelated burglary. Because the
police were unable to find any witnesses to the stabbing at the time, the homicide case
remained dormant until 1998 when the defendant was again arrested for burglary.
Authorities were able to locate the four original witnesses, three of whom identified the
defendant as the perpetrator. Although there was no other evidence connecting the
defendant to the stabbing, in 1999, the defendant was charged with second degree murder
and sentenced to a prison term of 25 years to life. The court eventually reversed the
defendant’s conviction and ordered a new trial on the grounds that they eyewitness
testimony was insufficient evidence. Approximately 75 percent of 215 DNA-based
exonerations (some of whom served time on death row) were cases of mistaken
identification that were accepted by juries as evidence that those innocent individuals

were guilty (Wells, Cutler, & Hasel, 2008). A study of actual eyewitness identification
attempts showed that one in five eyewitnesses selected an innocent person (Valentine,
Pickering, & Darling, 2003). According to the Innocence Project, the largest organization
devoted to proving wrongful convictions, mistaken identifications account for more
wrongful convictions than do false confessions, problems with snitches, and defective or
fraudulent science combined (Innocence Project, 2008). The National Institute of Justice
estimates that eyewitnesses in the United States implicate approximately 75,000
defendants every year (Department of Justice, 1999).
Many factors encompass legal decision making when involving eyewitnesses.
These individuals must accurately retrieve information from past events to make crucial
inferences. Memory, confidence, and facial recognition accuracy play an important role
during this process (Balfour & Pozzulo, 2006; Paiva, Berman, Cutler, Platania, &
Weipert, 2011). Figuring out how these factors affect eyewitness identification may give
more accurate information to legal decision makers, which could lead to fewer wrongful
convictions. Police investigators and jurors will understand how eyewitness testimony
accuracy is related to how much time has passed between the witnessing incident and
identifying the perpetrator in a lineup. Identifying the differences in eye gaze when
eyewitnesses are shown a familiar face compared to an unfamiliar face, and a younger
face compared to an older face of the same person, will give experts a reference for facial
recognition when working with eyewitnesses. Knowing this and how accurate the
individual’s memory is will inform legal decision makers on the credibility of the
eyewitness testimony and, hence, will lead to a safer environment for the general public
by making accurate decisions on criminal and civil cases. When children go missing,

investigators will know what type of photos to release to the public to best serve the
memory of its citizens to aid in the search. A majority of past research on eyewitness
memory has focused on using pictures of individuals where the pictures used in the
encoding/study phase and the recognition/test phase were taken at roughly the same time
(e.g., Alenezi & Bindemann, 2013; Bindemann, Avetisyan, & Rakow, 2012). This study
emphasizes the importance of aging in the eyewitness identification process while
focusing on facial features.
Features of the Face
The face is the most distinctive and widely used tool people use to identify others
(Bruce & Young, 1986). Recognition of familiar faces involves an interaction of different
functional components. One such component is a pictorial code, which is a description of
a picture that contains details such as lighting, grain and flaws of the photo, as well as
capturing the pose and expression portrayed. People also have the ability to put together
structural codes, which capture components of the face that help distinguish it from other
faces. Structural codes are identified in both pictures and real life situations. Bruce and
Young (1986) found that certain areas of the face provide more information about a
person’s identity than other areas. Internal features, which are areas of the face that are
less changeable (e.g., eyes, nose, mouth), are more informative for recognizing familiar
faces. People also apply information to an unfamiliar face, such as age, sex, personality,
intelligence and linking the face to a known individual. This is known as a visually
derived semantic code. In contrast, people use an identity-specific semantic code when
dealing with familiar faces. This may describe a person’s occupation, his/her social
circle, where he/she is typically seen, etc. People observe facial shapes and postures to

identify specific emotions that are evoked from a face, which Bruce and Young (1986)
refer to as an expression code. A combination of these functions is critical for facial
identification; however, there are flaws within this system.
Age Research
People are error-prone when attempting to match photos of unfamiliar faces,
which leads to misidentifications. For example, Bindeman et al. (2012) found substantial
differences in identification accuracy and variation in consistency between observers
when attempting to match photos of unfamiliar faces on different days. In other words,
they responded differently to the same faces on different days and identity accuracy
decreased as days increased.
In real life situations, significant time passes between the eye witnessing incident
and identifying the perpetrator in a lineup (Neave, 1998). This allows the culprit to
undergo changes in appearance either naturally or intentionally. Hair is a primary
indicator when describing a stranger by children and adults so culprits can easily alter
their appearance. When it comes to eyewitness identification, people provide less correct
identifications when the culprit’s appearance changes (Balfour & Pozzulo, 2006). This is
true for both children and adults when viewing lineups with a simultaneous presentation
and sequential lineup.
The target’s appearance can also change in terms of physical structures in the
face. In missing child cases, the child might look significantly different if several years
have passed between abduction and recognition. Pose, expression, and illumination
changes may occur when two photos of a person are taken years apart (Chellappa, Sinha,
& Phillips, 2010). Facial landmarks tend to drift with aging, especially between the ages

of 2 and 18, which appear to characterize the facial shape variations associated with
aging. Previous studies have concluded that more current photos of children result in
better facial recognition (Lampinen, Miller, & Dehon, 2012). In older adults, the texture
of the skin can change due to weight loss or gain, hair loss, make-up, etc. Several
environmental factors affect aging, including solar radiation, smoking, drug use, and
stress level (Albert, Ricanek, & Patterson, 2007). Environmental and biological factors
can help accelerate or delay the aging process.
Determining information about age may be necessary for efficient encoding of
faces. George and Hole (1998) found through their research that people are just as
accurate when shown a face and recognize the person at an older age as they are when
shown a face and recognize the person at roughly the same age. In contrast, recognition is
significantly lower when people view a face that is younger than the one first shown.
However, it is important to note what the age differences are. For example, there is much
more change in the first few years of life than there is change in a few years during the
middle of life. Given this non-linear, growth-related change throughout aging, the more a
face is perceived as changing from the one that was originally encoded, the more difficult
it is for one to accurately recognize it. With this being said, the findings of George and
Hole (1998) demonstrated that it is possible to recognize a face that has changed
structurally since it was last seen, because there are growth-related structure changes
associated with aging.
Eyewitness Confidence
Studies have shown that eyewitness confidence is malleable, and several factors,
including confirmatory feedback, repeated questioning, and public displays of

confidence, increase eyewitness confidence and erode the relation between confidence
and identification accuracy (Bradfield, Wells, & Olson, 2002; Wells, Memon, & Penrod,
2006). Most jurors are unaware of this and consider eyewitness confidence to be reliable
and are heavily influenced by this when evaluating culpability (Cutler, Dexter, & Penrod,
1990).
Confidence levels of the eyewitness at the time of identification tend to predict
greater accuracy than confidence levels during trial (Bradfield et al., 2002). Knowing
this, eyewitness confidence should be recorded immediately after identification so jurors
have the opportunity to evaluate the inflated confidence during trial in light of the
confidence level during identification, giving greater weight to the latter.
Eye-tracking Research
Tracking eye movements is an effective way to study face processing
(McDonnell, Bornstein, Laub, Mills, & Dodd, 2014). Previous studies have applied eye-
tracking technology to eyewitness memory research (e.g., Flowe, 2011; Flowe & Cottrell,
2011). In simultaneous lineups, participants spent more time looking at faces that were
positively identified than faces that were not identified (Flowe & Cottrell, 2011). This
study also found that participants view incorrectly positively identified faces longer than
correctly identified faces.
In regards to facial features, other studies found that participants are more
accurate at distinguishing faces when focusing on internal features (eyes, nose, and
mouth) as opposed to external features (hair; Fletcher, Butavicius, & Lee, 2008;
Nakabayashi, Loyd-Jones, Butcher, & Liu, 2012). However, external features may be
more important in face perception and recognition than internal features. People have

difficulty recognizing that the internal features of two faces are the same if they each
have different external features (Maurer, Le Grand, & Mondloch, 2002; Young,
Hellawell, & Hay, 1987). Also, when shown alone, external features of unfamiliar faces
are more recognizable than internal features shown alone (Ellis, Shepherd, & Davies,
1979; Young, Hay, McWeeny, Flude, & Ellis, 1985).
Present Study
These situations lead to questions of how well eyewitnesses can recognize a
person as that person ages, where eyewitnesses tend to focus during encoding and
recognition, if there are differences between viewing people at younger and older ages,
and how confidence relates to identification accuracy. This study will seek to answer
these questions using an eye-tracking machine and photos of the same individual at
different ages. We hypothesized the following:
1) In relation to the Neave (1998) article, the memory of a target’s appearance will
be less accurate as the age gap increases between encoding and recognition when
participants view the same target during encoding and recognition (i.e., doesn’t
include foils).
2) Using findings from the Bradfield et al. (2002) article, confidence will be at best
weakly associated with memory accuracy.
3) Drawing from the Bruce and Young (1986) article, individuals will gaze more at
external facial features (e.g., hairstyle and color) when shown an unfamiliar face
(not previously seen) and will gaze more at internal facial features (e.g., eyes,
nose, mouth) when shown a familiar (previously viewed) face.

4) Individuals will gaze more at external facial features when shown a younger face
and will gaze more at internal facial features when shown an older face of the
same person.
Method
Participants
Forty-seven undergraduate Psychology University of Nebraska - Lincoln students
were recruited and compensated with research participation credit. They had a mean age
of 19.79 (Std = 1.90) with a range from 18 to 28. Fourteen (29.79 %) of these participants
were male and 33 (70.21%) were female. Four (8.51%) were Asian American, 42
(89.36%) were European American and one (2.13%) was Hispanic American.
Materials
We used an eye-tracking machine connected to two computers to place on the
participant’s head with two cameras extended out in front of the face to track eye gaze.
We designated one computer to configure the eye-tracking machine while the other was
used for the participants to complete the tasks. Participants used a keyboard to submit
responses to questions. We used two computer programs for the study: Eyelink and
MediaLab. Eyelink was used for the first three phases, which involved using the eye-
tracking machine and for the participant to complete a survey. With consent, we gathered
various photos of University of Nebraska – Lincoln students of both genders and at
different ages for phase one and phase three. MediaLab was used to obtain demographic
information. A research assistant provided a consent form for each participant to sign and
date.
Procedure

Before each participant arrived to the study a research assistant set up both
computers and loaded Eyelink and MediaLab. The research assistant greeted each
participant and gave a brief overview of the study. The research assistant handed a
consent form to the participant that described the study in detail and asked each of them
to provide a signature. The research assistant then placed the eye-tracking machine on the
head of the participant and began calibrating the device. This involved adjusting the
cameras so they were pointed and centered on the eyes, fine-tuning the resolution to
produce a clear picture and setting the focus of the eye-tracker on the pupil. Then, the
research assistant calibrated the machine, in which he asked the participant to follow a
dot on the computer screen with their eyes while keeping their head still. Validation,
involving the same process as calibration, followed this step to ensure accuracy of the
eye-tracker. Once calibration and validation were successfully completed, the participant
proceeded through the four phases, which took approximately 30 minutes. We randomly
assigned each participant with a number (1-6), which placed them in separate groups of
viewing different photos. We performed preliminary analyses to counterbalance the
groups of photos to make sure that one group of 18 photos wasn’t different than the
others and to make certain that no individual target stood out. This ensured that age was
the variable being manipulated and not a particular photo of an individual.
1) Encoding phase. During phase one, the research assistant set up Eyelink for the
participant to view 18 photos of people presented randomly, one at a time, for three
seconds each. The photos consisted of both genders and at three different age groups (10-
13, 15-16, and 18-22).

2) Filler task. Phase two served as an unrelated filler task in which participants
completed a survey on their beliefs on how memory works in various contexts.
3) Recognition phase. In phase three, we tested participants on their memory of
the people they saw earlier, all presented as young adults. The photos of each of these
people were shown randomly, one at a time, for five seconds, mixed in with an equal
number of people they had never seen before (36 photos total). After each photo,
participants responded whether or not they recognized the person and gave a confidence
rating based on a 7-point scale.
4) Follow-up questionnaire. In phase four, participants completed a demographic
survey in which they recorded their age, gender and ethnicity. Participants also stated
whether or not they recognized any of the target photos from outside of the experiment.
Results
It was hypothesized that the memory of a target’s appearance will be less accurate
as the age gap increases between encoding and recognition when participants view the
same target during encoding and recognition (i.e., doesn’t include foils). As
hypothesized, there was a significant difference in accuracy across the age categories
with participants recognizing targets at a greater rate when previously seen targets were
in the old age category, F(2,880) = 52.77, p < .0001. The old age category (18-22) scored
higher than both the middle (15-16) and young (10-13) age categories, t’s > 3.43, p <
.0007. The middle age category scored higher than young age category, t(880) = 7.37, p <
.0001. Also, the foils category scored higher than the middle age category, t(1760) =
6.08, p < .0001 and the young age category, t(1760) = 9.06, p < .0001 yet scored lower
than the old age category, t(1760) = 3.30, p < .01. The old age category had a mean

accuracy score of 77.89%, the middle age category scored 47.92%, the young age
category scored 34.04%, and the foil category scored 67.54%. In full support of the
research hypothesis, the memory of a target’s appearance was less accurate as the age gap
increases between encoding and recognition, excluding the foils category.
The second hypothesis was that confidence will be at best weakly associated with
memory accuracy. Pearson’s correlation between confidence and memory accuracy was
r(1760) = .1191, p < .0001. Additionally, Pearson’s correlation between confidence and
memory accuracy for the old, middle, young and foil categories, respectively, were
r(285) = .31009, p < .0001; r(313) = .02029, p = .7207; r(285) = .12273, p = .0384;
r(881) = .1331, p < .0001. In support of the research hypothesis, confidence was at best
weakly associated with memory accuracy.
The third hypothesis was that individuals will gaze more at external facial features
when shown an unfamiliar face and will gaze more at internal facial features when shown
a familiar face. Contrary to the research hypothesis, there was no significant difference in
gaze time for external features (e.g. hairstyle and color) between encoding and
recognition, t(6759) = 0.11, p = .9107 with a mean gaze time percentage of 2.96% during
encoding and 3.05% during recognition. For internal features, there was no significant
difference in gaze time at the eyes between encoding and recognition, t(6759) = 0.68, p =
.4984 with a mean gaze time percentage of 46.08% during encoding and 45.57% during
recognition. There was no significant different in gaze time at the nose between encoding
and recognition, t(6759) = 1.61, p = .1085 with a mean gaze time percentage of 15.01%
during encoding and 16.24% during recognition. There was a significant different in gaze
time at the mouth between encoding and recognition, t(6759) = 4.51, p = .0001 with a

mean gaze time percentage of 18.27% during encoding and 14.81% during recognition.
Contrary to the research hypothesis, there was no significant difference in gaze time
between external and internal features when shown an unfamiliar face versus a familiar
face.
The fourth hypothesis was that individuals will gaze more at external facial
features when shown a younger face and will gaze more at internal facial features when
shown an older face of the same person. Contrary to the research hypothesis, there was
no significant difference in gaze time for external features in the young age category
between encoding and recognition, t(6759) = 0.20, p = .8414 with a mean gaze time
percentage of 3.17% during encoding and 3.35% during recognition. For internal
features, there was no significant difference in gaze time at the eyes in the young age
category between encoding and recognition, t(6759) = 0.62, p = .5362 with a mean gaze
time percentage of 45.41% during encoding and 46.22% during recognition. There was
no significant difference in gaze time at the nose in the young age category between
encoding and recognition, t(6759) = 1.25, p = .2115 with a mean gaze time percentage of
14.92% during encoding and 16.57% during recognition. There was a significant
difference in gaze time at the mouth in the young age category between encoding and
recognition, t(6759) = 5.54, p = .0001 with a mean gaze time percentage of 21.43%
during encoding and 14.12% during recognition. Also contrary to the research
hypothesis, there was no significant difference in gaze time for external features in the
middle age category between encoding and recognition, t(6759) = 0.48, p = .6300 with a
mean gaze time percentage of 3.80% during encoding and 3.17% during recognition. For
internal features, there was a significant difference in gaze time at the eyes in the middle

age category between encoding and recognition, t(6759) = 2.67, p = .0077 with a mean
gaze time percentage of 41.90% during encoding and 45.41% during recognition. There
was no significant difference in gaze time at the nose in the middle age category between
encoding and recognition, t(6759) = 1.17, p = .2425 with a mean gaze time percentage of
13.39% during encoding and 14.92% during recognition. There was no significant
difference in gaze time at the mouth in the middle age category between encoding and
recognition, t(6759) = 0.89, p = .3729 with a mean gaze time percentage of 20.26%
during encoding and 21.43% during recognition. Contrary to the research hypothesis,
there was no significant difference in gaze time between external and internal features
when shown a younger face followed by an older face of the same person.
Discussion
The results from these analyses provided support for some of the research
hypotheses. The third hypothesis, stating that individuals will gaze more at external facial
features when shown an unfamiliar face and will gaze more at internal facial features
when shown a familiar face, was not supported. This showed that there was no significant
relationship in gaze time between external and internal features when shown an
unfamiliar face versus a familiar face. Also, the fourth hypothesis, stating that individuals
will gaze more at external facial features when shown a younger face and will gaze more
at internal facial features when shown an older face of the same person, was not
supported. This showed that there was no significant relationship in gaze time between
external and internal features when shown a younger face followed by an older face of
the same person. However, the first hypothesis, stating that the memory of a target’s
appearance will be less accurate as the age gap increases between encoding and

recognition when participants view the same target during encoding and recognition, was
fully supported. This showed that there was a significant difference in accuracy across the
age categories with participants recognizing targets at a greater rate when previously seen
targets were in the old age category. Also, the second hypothesis, stating that confidence
will be at best weakly associated with memory accuracy, was supported. This showed
that confidence plays a weak role when determining memory accuracy.
Some of these findings can relate to previous research. The conclusion that
memory of a target’s appearance is less accurate as the age gap increases between
encoding and recognition relates to the research findings from Neave (1998). They both
found that as age increases between encoding and recognition, memory accuracy of
target’s appearance decreases. The conclusion that confidence was at best weakly
associated with memory accuracy relates to the research findings of Bradfield et al.
(2002). They both found that confidence cannot be a significant predictor of memory
accuracy. However, there were some contradictions. The conclusion that there was no
significant relationship in gaze time between external and internal features when shown
an unfamiliar face versus a familiar face or when shown a younger face followed by an
older face of the same person contradicted the research findings of Bruce and Young
(1986). Their study found that internal facial features are more informative for
recognizing familiar faces.
This study contributes to the field of psychological science in a number of ways.
The findings of the study support previous research, such as memory accuracy of a
target’s appearance increasing as the age gap between encoding and recognition
decreases. With this information, police investigators and jurors will understand how

eyewitness testimony accuracy is related to how much time has passed between the
witnessing incident and identifying the perpetrator in a lineup. When children go missing,
investigators will know what type of photos to release to the public to best serve the
memory of its citizens to aid in the search. Most importantly, this information may help
lead fewer wrongful convictions of innocent people. Also, the conclusion that confidence
is at best weakly associated with memory accuracy supported previous research.
Knowing this and how accurate the individual’s memory is will inform legal decision
makers on the credibility of the eyewitness testimony and, hence, will lead to a safer
environment for the general public by making accurate decisions on criminal and civil
cases.
The findings of this study, along with previous studies (Neave, 1998; Bradfield et
al., 2002; Bruce & Young, 1986), can be integrated to raise questions for future research.
To uncover the contradictions made between the current study and the Bruce and Young
(1986) study, it may be interesting for future research to look at the relationship between
gaze time on a particular facial feature and memory accuracy. For example, one group of
participants will view only the eyes during encoding and recognition; another group will
view only the nose during encoding and recognition, etc., and see how each particular
facial features relates to memory accuracy. Findings from a study of this nature will
provide individuals with information on what facial feature to focus eye gaze on more or
less when viewing an unfamiliar face during encoding or a familiar face during
recognition. Eyewitnesses to a crime will know which facial features to direct their
attention to during the crime and during identification procedures to produce higher
memory accuracy.

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