Work-in-progress, PupilWare-M: Cognitive Load
Estimation Using Unmodified Smartphone Cameras
Sohail Rafiqi1, Chatchai Wangwiwattana1, Ephrem Fernandez2, Suku Nair1, Eric Larson1
Computer Science and Engineering1
Department of Psychology2
Southern Methodist University
University of Texas
Dallas, TX
San Antonio, TX
Abstract—Cognitive load refers to the amount of information
a person can process or hold in working memory. Historically,
the psychology community has estimated this quantity
objectively by monitoring the involuntary dilations and
constrictions of the pupil using medical grade equipment
known as pupillometers. At the same time, researchers in the
HCI and UbiComp communities have hypothesized how
cognitive load sensing might be integrated into context aware
computing systems, but limitations of sensing cognitive load
ubiquitously and reliably prevent the mass integration of such
a technology. Our system, PupilWare-M, seeks to begin
bridging this sensing gap. We build upon a recent platform,
PupilWare, which measures a user’s sub-millimeter pupil
dilation from an unmodified camera. We update the
PupilWare sensing system with a calibration protocol that
brings pupillary responses of a diverse range of people and
lighting conditions onto a single 0.0-1.0 scale called CogPoint.
Furthermore, we update and optimize the algorithms
employed to run in real time from a smartphone. We validate
the calibration process using eight users in a controlled
experiment where cognitive load is simple to determine from
its situational context. Discussion of future work and
remaining challenges is then described.
Keywords—pupillary response; cognitive load; sensing;
image processing; context aware;
I. INTRODUCTION
Ubiquitous and mobile sensing systems have seen dramatic
and considerable accomplishments in recent years: sensing
heart rate with cameras [13], stress levels with wearable
technology [11], and even how interruptible an individual
might be [12]. However one aspect of ubiquitous sensing
that remains elusive is user context: understanding user’s
decision-making ability, affective states, and cognitive load
[1]. It is difficult to imagine methods for measuring these
quantities with both reliability and scalability (nonintrusive, low cost, etc.). However, the cognitive
psychology community has estimated these quantities in
situational contexts for decades using pupillary response.
Much research has been done to understand and validate the
correlation between pupillary responses and a user’s
cognitive and affective states, showing that involuntary
pupil dilation increases proportionally to cognitive load but
can also change with affective state or sexual arousal [2]–
[4][7]–[9]. Nearly all of this previous research around
detecting pupillary responses uses infrared light sources and
Figure 1—PupilWare-M stages running on iPhone 5S
high-resolution imaging, including remote gaze trackers
[2][18]. While gaze trackers provide accurate
measurements of pupil response, they are not yet used
ubiquitously, especially in mobile contexts. To this end, we
present PupilWare-M, a system that analyzes pupillary
response in real-time and determines cognitive load using
unmodified smartphone cameras.
PupilWare-M builds upon previous research called
PupilWare [14]. This system was a feasibility study for
measuring pupil responses from a webcam. In this study,
there are three contributions over the original feasibility
study:
• (1) Algorithms are refined to run in real time from a
smartphone and are evaluated on a smartphone with
eight users.
• We evaluate a custom calibration procedure that has two
purposes: (2) optimizing signal processing parameters
for different lighting and eye color, and (3) scaling and
projecting the pupil dilation from various users onto a
unified axis that estimates cognitive load similarly
across users.
PupilWare-M uses the embedded smartphone camera to
segment, enhance, and estimate the sub-millimeter dilations
of a user’s pupil when they are interacting with a
smartphone. These dilations are then mapped onto a new
axis called CogPoint that can be used across users for
ascertaining cognitive load in simple contexts. We validate
PupilWare-M against a high-resolution gaze tracker using a
classic experiment from cognitive psychology: the digit
span task [15][18]. Pilot experiments are first run offline on
four users to verify that real time pupil extraction can be
performed from a smartphone. These “offline” findings are
used to design a real time PupilWare-M prototype, which
we evaluated on eight different users (an “online”
experiment). We conclude that, in a normal room
environment, PupilWare is as accurate as an infrared-based
gaze tracker for assessing situational cognitive load.
Furthermore, simple calibration allows many users to map
to the same cognitive load scale, which is important for
future context aware systems that would use PupilWare-M.
Though significant research is still required to claim such a
technology could be used ubiquitously, PupilWare-M is a
first step in realizing context aware, cognitive monitoring
systems.
II. BACKGROUND AND MOTIVATION
Pupil response has been studied in a variety of different
situational contexts with some remarkable insights into
human cognition: understanding human intention [9],
efficient human computer interaction [8], understanding the
impact of interruptions on productivity [17], identifying the
emotional change of the user [6], understanding how people
reach decisions [1]. These works were informed by a vast
amount of previous research [2]–[4], [18] that studied
pupillary responses using infrared gaze trackers (either
medical grade, known as pupillometers, ~$4000USD, or
using high resolution eye trackers, ~$700USD). Because of
this research, (1) it is generally accepted that pupillary
responses accurately reflect workload induced by the task
and (2) pupillary responses change in response to a user’s
emotional state [19]. This pupillary change may not reflect
the actual emotion of the state, but signifies an emotional
transition occurred [ibid].
PupilWare-M is focused on using a mobile device to assess
pupillary response in real-time—but not at the expense of
practicality. PupilWare-M requires no augmentation to the
user nor does it require any additional hardware. Instead of
using infrared light (like a gaze tracker), PupilWare uses
computer vision and machine learning to extract the eye
and pupil diameter from an RGB camera.
It is important to note that PupilWare-M shares some
overlap with modern eye trackers, but is also quite distinct.
For example, recently Wood and Bulling [20] demonstrated
that eye gaze could be ascertained from the camera of a
tablet, without the use of an infrared light source. Their
prototype, EyeTab is closely related to PupilWare-M but
infers user’s gaze from measurements of the iris (not the
pupil). Wood and Bulling’s system has clearly influenced
the inception of PupilWare-M by demonstrating that eye
segmentation and processing is possible from mobile
devices, however, PupilWare-M employs methods that are
distinct from iris detection. Furthermore, PupilWare-M is
validated for its ability to measure sub-millimeter pupil
dilations, not track saccades or foveation.
III. DATA COLLECTION
In order to validate the PupilWare system we conducted
two user studies:
(1) A study in which user data was collected with a normal
web camera for offline analysis (four users; data used only
for testing processing speed and optimizing parameters to
run on a mobile device).
(2) A follow-up study that used a smartphone for real-time
cognitive analysis (eight new users; data used for evaluating
ability to estimate pupillary response in online study).
Each study artificially induced cognitive load using
memorization of spoken digits, known in psychology as the
digit span task. In this task, a participant is presented with a
sequence of spoken digits and asked to memorize and then
repeat the sequence out loud. The act of memorization
induces cognitive load in a consistent manner—though the
effort and amount of pupil dilation are user dependent. Each
participant was presented with increasingly longer (and
therefore more difficult) sequences to memorize, varying in
length from 5, 6, 7, to 8 digits (with four iterations per
sequence length using different random digits each time).
Initially we also presented a 9-digit sequence but nearly all
participants had difficulty with the task. They would “giveup” memorizing and the cognitive load induced became
dependent on the effort of the user. This is consistent with
other studies of cognitive load [2][4]. As such, we only
present results for up to length 8 sequences. This results in
128 trials of the digit span task (8 users x 4 sequence
lengths x 4 iterations).
At the start of the session, participants were also asked to
“relax and stare at the phone for about 10 seconds.” This
“baseline” data is used to calibrate the parameters of our
algorithm (discussed in detail later on).
Table 1—Participant eye color
Participants Brown
Black
Blue
8
1
2
4
Green
1
For measuring the ground truth pupil dilation, we use a
Gazept Remote Eye tracker [21] that is calibrated using a
medical grade pupillometer with accuracy down to 0.1 mm
of pupil dilation. At the beginning of the test the distance
between participant’s eyes (i.e., pupillary distance) was
measured using a digital caliper. This is used for converting
pixel data to millimeters. During the experiment the
calibrated gaze tracker and the smartphone camera
collected the participant’s pupillary feature data
simultaneously. The experiment was conducted in several
environments: (1) an office under normal (but uncontrolled)
lighting conditions and (2) in a conference room surrounded
by open windows. Prior to commencing the test and
between iterations we ask participants to relax.
For the study, participant demographics are as follows: 8
participants (5 female/3 male) ranging in age from 19 to 23.
Eye color is described in Table 1.
IV. ALGORITHM
In this section we present the PupilWare-M algorithm.
PupilWare-M uses many of the same processing techniques
as the original PupilWare algorithm [14]. However, there
are key differences in PupilWare-M that are distinct:
• Adaptive filtering algorithms have been added to
denoise the pupil.
• An iterative calibration protocol has been added to help
adapt the PupilWare algorithm to different users and
ambient light conditions.
We briefly present an abridged explanation of the
PupilWare algorithm before explaining new algorithm
implementations in more detail.
A. Abridged PupilWare-M Algorithm Explanation
PupilWare-M employs computer vision to: (1) eliminate
noise via preprocessing, (2) segment the left and right eyes
from a video frame, (3) use a “modified starburst”
algorithm to estimate pupil diameter, and (4) eliminate
outliers in the pupil diameter estimate. Pupil size is
obtained from a sequence of images from an iPhone 5S face
time camera with resolution 1028x720 (i.e., 720p) running
at 15 frames per second. We employ OpenCV [22] and
GPU accelerated filtering in Apple’s CoreImage library for
image processing.
Segmentation: As in the original study [14], we start with
locating and extracting the face using a Haar-Cascade
detector. We then constrain the portion of the face where
the eyes are most likely to reside based upon the face
bounds. We then convert to a gray scale image, and the eye
center is estimated using dynamic thresholding,
morphology, and the centroid of connected components as
described in [14]. We denote this area as the eye region-ofinterest or eye-ROI.
Eye-ROI preprocessing: One of the difficulties of
capturing pupil size from a camera without infrared light is
dealing with occlusion from eyelashes, light reflection, and
shadow. Moreover, for dark eyes, the intensity difference
between iris and pupil makes it difficult to find a clear
boundary. In order to get higher quality results, we
preprocess the eye regions to reduce reflection and increase
contrast. These preprocessing steps include median filtering
with a 3x3 kernel and histogram equalization of the eye
region. The area around the seed point is typically about
120x120 pixels and the pupil is typically 15x15 pixels.
PupilWare Starburst Algorithm: The starburst algorithm
is a method typically employed in infrared eye trackers after
the pupil is illuminated with infrared light [23]. Intuitively,
the starburst algorithm uses a seed point in the eye to begin
“looking” for the edges of the pupil at various angles around
the seed point. It marches in many directions, looking for
strong edges (thresholding on the image gradient). It then
tries to formulate a better estimate of the pupil center from
the edges points and repeats the march iteratively. Outliers
are removed using RANSAC and the remaining boundary
points are used in an ellipse-fitting algorithm [24]. The
Starburst algorithm is run separately on each eye. We
estimated pupil diameter by calculating the diameter of the
fitted circles and averaging for each eye.
Modifications in PupilWare-M: We have modified the
starburst algorithm to only march from 35 degrees to 125
degrees to avoid the shadow from eyelashes and be more
robust to squinting. The exact threshold value for an “edge”
in the image gradient is optimized during calibration. We
also adjusted the “march” of the starburst algorithm to be
biased towards finding edges that are relatively “far” from
the seed point. That is, we decrease the magnitude of the
threshold needed on the image gradient for a point to be
considered an “edge.”
This location bias is achieved using a 10x10 pixel Gaussian
kernel, G(x,y), centered at the current seed point, where x
and y are the pixel positions with origin at the Gaussian
center (the maximum of the Gaussian is normalized to be 1
with covariance set during calibration). We weight the
image gradient by 1- G(x,y). As a result, edges close to
the center are more likely to be ignored and edges that are
farther away from eye center are more likely to be chosen
as “pupil edges.” This also helps eliminate edges caused by
light refection (and noise in general) in the pupil. The
covariance matrix of this Gaussian kernel is adjusted during
the calibration phase. We assume the covariance is
diagonal, adjusting standard deviation in the vertical and
horizontal axes of the eye-ROI, but off diagonal covariance
elements are assumed to be negligible.
Post Processing: PupilWare-M first converts the ellipse
diameters from pixels to millimeters. This is achieved by
calculating the Euclidean distance (in pixels) between the
user’s left and right pupil centers, as calculated from the
Starburst algorithm. We scale the estimated pupil diameters
by the known value between the eyes (measured via
calipers in the study session). Note that this approach
assumes the eyes lie in a plane perpendicular to the camera,
necessitating that we detect and discard large head pose
changes. This is achieved using median filtering over a one
second moving window. After filtering, the original and
raw feature values are compared. Any value that changes
more than 5% after median filtering is discarded. The
missing values are then imputed with linear interpolation.
Calibration: In PupilWare-M we introduce the calibration
process to accommodate for light variability in the mobile
environment. The purpose of the calibration process is to
dynamically find the optimal parameters for thresholding
and the Gaussian kernel, G(x,y). That is, three values are
investigated: the threshold for designating an “edge” and
the two diagonal elements of the Gaussian covariance
matrix. The calibration uses a 10-second recording of the
user relaxing and staring at the screen of the phone (i.e., the
baseline video taken at the start of the experiment). We then
cognitive psychology, relative change in the pupil diameter
during the course of a task is typically used for measuring
cognitive load [19], although no standard is used by all
researchers. The percent change of pupil dilation for the ith
frame, Di, from baseline is calculated by
Di =
Figure 2—Calibration overview (left) and results for one
user as displayed on smartphone (right). Top three iterations
shown with selected parameterization result in green.
process the baseline video over many iterations, using a
random grid search to change parameters [25]. Each
parameter can take on 5 different values, resulting in 125
possible combinations for the grid search. Each set of
parameters is allowed to run to completion (on the
smartphone) and the pupil diameter measurement is saved
in the smartphone document directory. To keep the runtime
manageable, we stop the randomized search after 45
seconds of searching. We then choose the parameter set that
had the minimum Mean Absolute Deviation Ratio (MADR)
in the pupil diameter in the calibration baseline videos.
MADR =
MAD(x)+ | median(x) |
| median(x) |
Where x is the estimated pupil diameter signal, and MAD is
Mean Absolute Deviation. We choose the minimum
because, during calibration, the user’s pupil should not be
dilating or constricting. Therefore, the parameter set with
the most consistently small deviations is likely not finding
noise boundaries, such as shadows, eyelashes, or
reflections. Moreover, these parameters help PupilWare-M
adapt to eye color because the gradient threshold is chosen
dynamically. These parameters are then saved to the
smartphone for the current user.
We use this calibration process at the beginning of each
online experiment. The results of this calibration are used
for a participant for all remaining digit span task
experiments. As a qualitative example, Figure 2 shows the
result of three sets of parameters during the calibration
process. Once the calibration process is completed, the best
parameter values are set as default for any future video
processing until next calibration is performed.
V. COGNITIVE LOAD DETERMINATION
We now discuss the implementation of a novel scale called
CogPoint that can be used to map the relative cognitive load
of different users onto a single, comparable dimension. In
di − min(d5sec )
min(d5sec )
where di is the estimated pupil diameter, in millimeters, for
the ith frame, and d5sec is the pupil dilation during the first
five seconds of the digit span task. The max(Di), then, is the
maximum deviation from baseline during a given task. To
customize this for each individual participant we introduce
a value called CogPoint that ranges in value from 0.0 (low
cognitive load) to 1.0 (high cognitive load). The CogPoint
range is calibrated using two videos: max(Dbaseline) from the
baseline video and max(D8digit) is from one iteration of the
8-digit span task, which should induce the highest cognitive
load. We then define CogPoint for each frame, i, as
CogPoint(i) =
Di − max(Dbaseline )
max(D8digit )
In this way, using two brief videos, PupilWare-M can be
calibrated to measure cognitive load of any individual,
personalized for lighting, eye color, and pupillary
percentage change to simple cognitive tasks. CogPoint does
not account for changes in brightness or context of a scene,
only the current situated environment.
VI. RESULTS
In the following, we present the results of our eight person
user study. In the first section we define the results of
baseline calibration using the gaze tracker as a ground truth
for the selected parameters of the algorithm. The second
section compares pupil size changes as a result of induced
cognitive load from the digit span task. Finally we discuss
the utility of the CogPoint estimation.
A. Initial Calibration
As part of the initial calibration we use a randomized grid
search to select the parameter set with the most optimal
MADR, as described. Explanation: Figure 3 shows one
result of this calibration for one participant. The x-axis
represents the video frames and y-axis represents the pupil
size in mm. Solid lines represent the result of one iteration
of the grid search. The four parameter sets with the lowest
MADR are graphed (P1-P4). The red dashed line represents
the ground truth from the gaze tracker. Bold green line is
the best result automatically chosen (P4 in Table 2).
Result: The parameters that result in the lowest MADR
score also correspond to the results that most closely match
the gaze tracker. Interestingly, P4 and P1 seem to be
relatively good choices, whereas P2 and P3 deviate
considerable from the gaze tracker. Looking back, these
corresponded to the algorithm getting confused about the
pupil/iris boundary from a reflection in the eye. It is
Table 2—MADR Values
MADR
P1
1.1034
P2
1.1150
P3
1.1126
P4
1.0907
Figure 3–Qualitative Calibration comparison
encouraging that these two series also had higher MADR.
Implication: The calibration successfully selects
parameters that correspond to the gaze tracker, even though
it has no knowledge of the underlying gaze tracker ground
truth. The results presented here are highly similar for all
participants. However, even though these results show a
close relationship between the best parameters picked by
the application and the ground truth, more testing is
required to ensure that this procedure works in varying
lighting conditions or in dynamic lighting conditions.
B. Pupil Dilation Comparison
In this section we evaluate the accuracy of capturing realtime changes in the pupil size during the digit span task.
Specifically, we want to know how the PupilWare-M
results compare with the gaze tracker.
Explanation: Figure 4 shows the comparison between the
percentage pupil dilation measured by the gaze tracker
against the mobile data calculation. This plot is known as a
Bland-Altman plot and is used for comparing a ground truth
device to a sensor under test in the medical community. The
y-axis represents the point-by-point difference of the
percentage pupil change between the gaze tracker and
PupilWare-M for every iteration and every participant. The
x-axis is the percentage pupil size change measured by the
gaze tracker. The graph represents the point-by-point
difference for each participant, where each participant is
represented by a different color on the graph. Result: The
interquartile range of the residual difference is -0.05881 to
0.05886 with the mean difference of -0.0008. PupilWare-M
and the gaze tracker have remarkable point-by-point
similarity, with very few differences greater than 5%. The
majority of outliers come from one participant with black
eye color (i.e., very dark brown).
C. Cognitive Load Classification
Explanation: Figure 5 shows the boxplot of maximum
CogPoint values for one participant (as a qualitative
example), and all participants. The y-axis is maximum
CogPoint during an iteration of the span task and x-axis is
length of the digit sequence used in the span task. Result:
Figure 4 – Gaze and Mobile device comparison
The CogPoint values are spread evenly from 0 to 1 for all
participants, with increasing difficulty of the cognitive
memorization task. For instance, 5-digits is, on average,
lower than 6, 7, and 8 digits.
Implication: The data shows considerable overlap between
the five and six digit cognitive load induced. While this
could be an indication that CogPoint fails to distinguish the
difference, it could also be that the tasks illicit similar
cognitive load. For example, in Kahneman’s original study
of cognitive load [4], pupil response between the 5 and 6
digits sequences overlapped completely. The results also
point to some natural breakpoints to classify the cognitive
load induced. A CogPoint value less than 0.3 may be
considered “low or relaxed.” A CogPoint value spanning
0.3-0.6 could be considered “medium” and corresponds to
memorization of 5-6 digits. And a CogPoint above 0.6
might be “maximal or high” cognitive processing
corresponding to memorization of 8 digits.
VII. LIMITATIONS
There are many factors contribute to the pupil dilation, such
as the ability of recognize pattern in some participants, the
level of concentration, emotion could also affect the
cognitive load. Therefore, many unaccounted for variables
may have contributed to deviation in the calibration of digit
span tasks and cannot be separated from “poor
performance” of the algorithm. Another limitation of our
experiment is the sample size. We cannot perform statistical
tests that involve subgrouping without collecting more data.
Figure 5—Maximum CogPoint grouped by experimental task
(5-8 digits)
These experiments were conducted in mostly consistent
lighting conditions; it is unclear how PupilWare will
perform in dynamic lighting or low light conditions.
VIII. CONCLUSION
In this paper we presented PupilWare-M, a system that
determines cognitive load of the user using pupil dilation
measurements. PupilWare-M uses an unmodified camera in
a mobile device. We used classic psychology experiments
to artificially induce the cognitive load. During
experiments, a remote eye tracker was used to validate the
accuracy of PupilWare-M. A novel calibration process was
used to improve the accuracy of cognitive load
classification. Our results demonstrated a high level of
correlation between measurements from PupilWare-M and
the remote gaze tracker. We also provided a way for
applications to define cognitive load level for their own
specific purposes.
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