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On the Applicability of Computer Vision based Gaze Tracking in Mobile Scenarios


Abstract and Figures

Gaze tracking is a common technique to study user interaction but is also increasingly used as input modality. In this regard, computer vision based systems provide a promising low-cost realization of gaze tracking on mobile devices. This paper complements related work focusing on algorithmic designs by conducting two users studies aiming to i) independently evaluate EyeTab as promising gaze tracking approach and ii) by providing the first independent use case driven evaluation of its applicability in mobile scenarios. Our evaluation elucidates the current state of mobile computer vision based gaze tracking and aims to pave the way for improved algorithms. In this regard, we aim to further foster the development by releasing our source data as reference database open to the public.
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On the Applicability of Computer Vision based
Gaze Tracking in Mobile Scenarios
Oliver Hohlfeld, Andr´
e Pomp,
Agila Bitsch Link
RWTH Aachen University
Aachen, Germany
Dennis Guse
TU Berlin
Berlin, Germany
Gaze tracking is a common technique to study user interac-
tion but is also increasingly used as input modality. In this
regard, computer vision based systems provide a promising
low-cost realization of gaze tracking on mobile devices. This
paper complements related work focusing on algorithmic de-
signs by conducting two users studies aiming to i) indepen-
dently evaluate EyeTab as promising gaze tracking approach
and ii) by providing the first independent use case driven eval-
uation of its applicability in mobile scenarios. Our evaluation
elucidates the current state of mobile computer vision based
gaze tracking and aims to pave the way for improved algo-
rithms. In this regard, we aim to further foster the develop-
ment by releasing our source data as reference database open
to the public.
Author Keywords
Gaze tracking; user study; Quality of Experience
ACM Classification Keywords
H.5.2 User interfaces: Input devices and strategies
Gaze tracking is often applied to study user interaction, but
is also increasingly used as system interaction technique.
Whereas the first allows understanding user behavior and in-
tention as well as to identify user interface issues, the latter
extends currently available user interfaces (UI) by providing
an additional input modality (e.g., Samsung Smart Scroll or
Smart Stay). Gaze tracking has also been proposed for con-
tinuous behavioral authentication [17].
Computer vision (CV) based gaze tracking solutions estimate
the users’ gaze direction based on the iris positions in vi-
sual images, e.g., as captured by the front-facing camera. By
solely utilizing hardware built into mobile devices, they offer
aportable and low-cost alternative to traditional systems that
require additional hardware (e.g., head mounted gaze track-
ers). Thus, gaze tracking can be easily used on commodity
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hardware, which opens the opportunity to address new use
cases. An example of such a desired use case is the diag-
nosis of reading disorders in schools where tablets replace
textbooks. While the related work (see e.g., [11, 12, 15, 31,
33]) has focused on showing the feasibility of the proposed
algorithms, a comprehensive study assessing their practical
applicability is still missing.
Hence, the goal of this paper is to fill this gap by providing the
first independent use case driven assessment of the practical
applicability of CV based gaze tracking in mobile scenarios.
We base our evaluation on a widely used Nexus tablet com-
puter and EyeTab [33] as recent and promising gaze tracking
algorithm. We then evaluate its performance in two consecu-
tive user studies.
Our first user study focuses on assessing crucial factors that
impact the use of mobile gaze tracking, e.g., the achieved
accuracy subject to varying conditions including lighting,
glasses, and two viewing distances. The main finding of
this study is that glasses—which were cautiously removed
in related work [11, 31, 33]—impact the gaze tracking per-
formance only marginally, whereas lighting and distance can
have a significant impact. Moreover, there are some limita-
tions for these kinds of applications. For example, we mea-
sure that accuracy is in the order of only 700 px (15) at
a low viewing distance of only 20cm. This largely limits the
applicability of the approach for classical applications such
as measuring visual attention in user interface design. Fur-
ther, the accuracy decreases from the top to the bottom of the
screen for a front-facing camera mounted at the bottom.
Informed by our findings in the first study, we conduct a sec-
ond user study in which we assess the applicability to prac-
tical use cases that only require a low spatial accuracy. We
focus on two particular use cases with increasing complexity,
i.e., i) detecting whether a user gazes at the screen and ii) de-
tecting reading patterns for the diagnosis of dyslexia. For the
more complex case of detection of reading patterns, we find
on the one hand that the detection of word fixations is more
prone to suffer from the limited accuracy of EyeTab. How-
ever, on the other hand, we find that line progression pattern
can be detected. Likewise, we found good accuracy to detect
even more simpler cases like identifying if the user focuses
on the device or gazes besides it. Both use cases are based
on detecting patterns that can be applied in a broader set of
use cases. Examples of envisioned input modalities include
locking/unlocking screen or automatic scrolling adaptive to
the users’ screen reading speed.
Our results on the limited accuracy of EyeTab motivate the
development of improved gaze tracking algorithms. Never-
theless, there is a lack of a standardized evaluation method-
ologies and a lack of reference data. We aim to partially fill
this gap by i) releasing our test framework as open source [2]
and ii) releasing our data as reference database [1]. We hope
that our contribution will be further enriched with other ad-
ditional data sets and pave the way for more accurate gaze
tracking algorithms to be run on low-cost mobile devices.
Gaze tracking is used in numerous scientific fields involving
a multitude of different application scenarios. Classical ap-
plication areas include psychology (e.g., for detecting read-
ing disorders [23]) or human computer interaction (e.g., for
improving interface design [4] or as input modality [8, 6]).
These classical applications involve the use of dedicated (typ-
ically head-mounted) gaze tracking hardware (e.g., tracking
glasses). To allow for controlled conditions, these studies
are typically conducted in laboratory settings. More recently,
lab based studies are more often complemented by the (more
challenging) application of gaze tracking in mobile settings.
Recent example areas include i) detecting document types
from reading pattern [19], ii) estimating cognitive load and
user experience [21], iii) evaluating visual attention [16] and
allowing pervasive eye-based interaction [27, 7].
The use of hardware-based gaze trackers has been recently
complemented by the development of computer vision (CV)
based gaze estimation algorithms. These algorithms are par-
ticularly appealing for mobile scenarios since they can run
on (unmodified) mobile devices. Proposed approaches are
based on neuronal networks, linear regression, or geometric
models. Based on geometric models, the open-source ap-
proach EyeTab [31, 33] estimates a user’s gaze points either
directly [31] or by employing a trigonometric approxima-
tion [33]. The performance (spatial and temporal accuracy)
of each approach was evaluated in a dedicated user study [31,
33]. Depending on the study, they evaluated both different
lighting conditions (sunlight, indoors bright, indoors dark)
and distances (15 cm, 20 cm) or fixed ones (indoor at 20cm).
The studies revealed an average spatial accuracy of <13.8
and a temporal accuracy of 12 fps. Further, a significant dif-
ference for the tested distances was found whereas the differ-
ent lighting conditions had no significant impact on the mea-
sured accuracy. Based on linear regression, a feature-based
gaze tracking approach is proposed and evaluated in [15]
(source code not available). The evaluation reveals an aver-
age spatial accuracy of 2.27for a distance of 30 cm. Based
on using neuronal networks, an open source gaze tracking ap-
proach is proposed and evaluated in [11, 12]. By evaluating
much higher distances of 50 cm and 60 cm, they find an aver-
age spatial accuracy of 4.42to 3.47and temporal accuracy
of 0.23 fps to 0.7fps, depending on the training set size.
The applicability of CV based gaze estimation in real-world
use cases was addressed only in a few studies focusing on
i) eye gesture recognition [28] and ii) gaze based password
entry [31]. However, a broad evaluation outlining appropriate
applications for the proposed algorithms is still missing.
We complement the body of related work by i) independently
evaluating the EyeTab gaze tracking performance and ii) by
providing an independent use case driven evaluation of its ap-
plicability in mobile scenarios. Our evaluation is based on al-
gorithmic variations of the EyeTab approach [31, 33] due to
the availability of its source code, the extensive and promis-
ing evaluation as well as the high temporal accuracy.
EyeTab’s allows live gaze estimation based on images cap-
tured at the device’s front-facing camera. For each captured
image, a rough eye pair Region of Interest (ROI) is detected
by applying a Haar Classifier (as implemented in OpenCV).
Once an eye pair is detected, the pupil is detected for each
eye by using either the isophotes method [29], the gradients
approach [26], or using a combination of both. Depending
on the used approach but also the detected eye pair ROI res-
olution, this step has the highest computational complexity.
Since longer processing times reduce the temporal accuracy
(i.e., the fps rate) of the EyeTab algorithm. For example, the
isophotes method is faster whereas the gradients approach is
more accurate. Since these algorithms work on a re-sized
(scaled) image of the detected eyes, the performance of both
approaches can be optimized by further downscaling the eye
pair ROI. This downscaling can, however, lead to inaccurately
detected pupils.
After localizing the pupils, the rough eye regions are re-
fined to smaller ones, which increases the performance for the
next steps. In these refined eye regions, EyeTab searches for
the limbus eye feature by using a non-deterministic Random
Sample Consensus (RANSAC) ellipse model fitting algo-
rithm. Based on the detected ellipses, EyeTab determines the
visual axis for each eye by either using a geometric model ap-
proach [31] or a simplified trigonometric approximation [33].
The calculated visual axes for each eye are then used by Eye-
Tab to determine the Point of Gaze (PoG).
Since EyeTab is available in two implementations [32] with
varying feature sets (i.e., C++ and Python version). For sim-
plified Android portability, we extended the C++ version [33]
with features of the Python version [31]. Concretely, we
added the following features. i) We added the isophotes
pupil detection method and a combination of the gradient and
the isophotes method. ii) We added the simplified geometric
gaze detection model. The extended implementation allows
us to evaluate a broad set of different algorithm combinations.
We further improved the gradients approach by replacing the
EyeTab implementation based on [26] with Hume’s [13] im-
plementation of [26] since it provided better results.
To perform the user studies, we developed a test framework
for the widely spread Android platform and integrated Eye-
Tab into it. The goal of this framework is to offer gaze track-
ing algorithm developers a powerful tool that handles com-
mon functionalities needed for testing. Concretely, the frame-
work offers the following core features:
Figure 1. Setup for study 1 Figure 2. Study 1 UI
Perform camera calibration
Broad set of tests that can be used for user studies
Live gaze tracking with additional screen recording
Post processing gaze tracking including recording of cam-
era and screen as well as matching of both recordings
Capturing diverse sensors, e.g., accelerometer, gyroscope,
lighting sensor
Easy extensible for adding new test cases
Easy extensible for adding new gaze tracking algorithms
Our framework is available as open-source software [2].
The first user study serves as baseline for discussing the appli-
cability of mobile CV based gaze tracking by assessing Eye-
Tab influence factors and the achieved accuracy. We base
this accuracy evaluation on still pictures where subjects are
asked to gaze at predefined screen points rather than videos
that would introduce more noise to our measurements.
Study Design
Our assessment is based on a laboratory user study assess-
ing the performance of EyeTab on a tablet computer. The
used Nexus 7 (2013) tablet has a screen resolution of 1200 ×
1920 px and a physical screen size of 9.5×15.1cm. Its front-
facing camera has a resolution of 720 ×1280 px. The tablet
was fixed on a height adjustable table in front of the subject
(shown in Figure 1). The distance between the subject and
the tablet is adjusted by displaying a distance dependent face
overlay during the entire test following [33] (see Figure 2).
Condition Description
Light Source
LW Light wall
LC Light ceiling
LB Both
Viewing Distance SSmall = 20 cm
NNormal = 30 cm
Wearing Glasses Yes
Pupil Detection
Isoph80 Isophotes, scale = 80 px
Grad50 Gradient, scale = 50 px
Grad80 Gradient, scale = 80 px
Comb5080 Grad50 + Isoph80
Gaze Tracking
Geo Geometric model
Approx Trigonometric approx.
Table 1. Study 1 conditions
The study is based on a within subjects design in which each
subject had to perform six tests for two different distance
conditions and three different lighting conditions. We fur-
ther evaluate the influence of glasses and different algorith-
mic variations (see Table 1). This study relies on a modified
version of our test framework (see [3] for the source code),
which displays a 5×5grid of yellow dots (see Figure 2).
For each measurement, one dot is colored in blue, which the
participant should fixate and then press a button to confirm
fixation. Upon confirmation, a picture is captured with the
front-facing camera and the current luminance is measured
using the built-in light sensor. After completing all 25 dots,
the test is repeated for the next condition. Since the used
EyeTab algorithm expects video inputs, we repeat each of the
25 still pictures per condition 30 times to create a combined
video of 25 seconds length at 30 fps.
The study was conducted in a window free room having three
LED lighted milky glass walls and one white concrete wall.
The glass walls have an integrated white LED lighting allow-
ing an exact regulation of the lighting conditions throughout
the study. In addition, the room features additional ceiling
lamps that allow the emitted light to be regulated. These con-
ditions allow studying the influence of different lighting con-
ditions on the gaze tracking performance.
Ten subjects (9 male, 1 female) participated in the study with
an average age of 27.4 years (SD = 3.5years). To mitigate a
potential bias, we selected subjects with different eye colors:
five persons with brown, three with green, and two with blue
eyes. Half of the subjects wore glasses whereas the other half
did not require any optical aid. One subject was removed
from LW as the viewing distance was not kept correctly.
Error Rate Evaluation
The first evaluation tests whether the distance or the lighting
conditions influence the error rate. We define the error rate
as the percentage of frames for which EyeTab was unable to
estimate a gaze point, e.g., due to an invalid detected pupil or
undetectable eye pairs.
Distance Influence: We first evaluate the influence of dis-
tance on the error rate for the different light sources, pupil
detection algorithms, and gaze tracking algorithms. By ap-
plying a non-parametric Mann-Whitney U test we find a sig-
nificant difference between the small (S) and the normal (N)
viewing distance in the error rate (p < 0.05). This in-
fluence is irrespective of the light condition and is in line
with [33] that found similar results for smaller distances of
15 cm and 20cm. We additionally determined the average er-
ror rate for all subjects per algorithm. Over all algorithms, the
(min, max) per-subject average error rate was for S(1.47%,
15.32%) and for N(53.09%, 77.29%).
Light Source Influence: Similarly, we next evaluate the in-
fluence of three different light sources for the same set of
algorithms. We did not observe any significant difference
on the observed error rate for the Ndistance (p0.05).
For the Sdistance, however, we observed a significant differ-
ence between LW and LC for the pupil detection algorithms
Comb5080,Grad50 and Grad80, irrespective of the gaze
tracking algorithm (Approx or Geo). Only for the Isoph80
algorithm, no difference between LW and LC was found
(p > 0.05). Differences between LW/LB or LC/LB could not
be found for any algorithm combination.
Accuracy Evaluation
Light Source: Compared to the error rate evaluation, we
find a significant difference between the LW and LC light
sources for the algorithm combinations Comb5080 Approx,
Comb5080 Geo,Grad 50 Approx and Isoph 80 Approx
whereas we do not find statistically significant differences for
the other algorithm combinations.
Algorithmic Influence: We limit our discussion of algo-
rithmic differences to LW (e.g., as typical in labs and class-
rooms). We further restrict the discussion to the Sdistance
due to lower error rates. For these settings, we find large
differences in the achievable gaze tracking accuracy as ex-
pressed by the RMSE, defined as the distance in pixels be-
tween the estimated and the expected gaze point. For exam-
ple, for the light source LW,Grad50 Approx yields an aver-
age accuracy of 705 px (15.19), whereas Isoph80 Geo yields
1117 px (24.06). Analyzing the differences between the two
gaze tracking algorithms (i.e., Geo and Approx) for four pupil
detection algorithms, we find a significant difference for the
Comb5080 (p= 0.034), Grad50 (p= 0.008), and Grad80
algorithm (p= 0.022). No significant difference was only
found for Isoph (p= 0.064).
We next evaluate if there is a difference between the differ-
ent pupil detection algorithms. Comparing the Isoph algo-
rithm to Grad50 (p= 0.001), Grad80 (p= 0.001), and
Comb5080 (p= 0.013) yielded significant differences. We
could not find a difference between Grad50 and Grad80 and
Comb5080, and between Grad80 and Comb5080 (p0.05).
This confirms [33] in which the Isoph algorithm was found to
run faster but less accurate.
Influences of Glasses on Accuracy and Error Rate
We next evaluate the influence of glasses on the gaze tracking
performance. This is a relevant since subjects wearing glasses
were cautiously excluded in related studies (see e.g., [31, 33]
or [11, 12]). In our study, subjects wearing glasses achieved
an average error rate of 3.81 ±4.09% (0±0% for non-glass
wearing subjects) and an average accuracy of 704 ±84 px
(707±137 px, respectively). A statistical test (Mann-Whitney
p= 0.903) suggested no significant difference. The results
show that excluding subjects wearing glasses is unnecessary
since no significant difference in the observed gaze tracking
performance could be observed if visual properties were de-
tected (e.g., no glass reflections covering the eye pairs).
There are, however, cases in which glasses prevent required
visual properties to be detected, e.g., due to strong light re-
flections in the glasses concealing the eyes. We observed such
a case for one subject where the reflections resulted in high
eye detection error rates. The subject is, however, not in-
cluded in the above results as the subjected aborted the test.
Our findings suggest glasses to only impact the gaze tracking
performance when they prevent visual properties from being
(a) Focus on Device (b) Line Test (c) Point Test
Figure 3. Schematic study 2 UI
Our main observation is that a low viewing distance (i.e.,
20 cm) between the tablet and the user is required to
achieve a reasonable accuracy and low error rates when using
EyeTab. Using a tablet at such close distances feels unnat-
urally close for many use cases. Despite the closeness, the
resulting accuracy is still as low as ±700 px (15) on average
(for a screen resolution of 1920 ×1080 px). The achieved ac-
curacy is further decreasing from the top of the screen to the
bottom due to a decreasing angle between the gaze position
and the bottom mounted camera. These observations render
the usage of EyeTab infeasible for many HCI use cases, e.g.,
for measuring visual attention in user interface evaluations.
We expect improved computer vision algorithms and future
front cameras having higher spatial resolutions to improve the
gaze tracking performance. For example, using higher reso-
lution front cameras can increase the viewing distance as eye
features (e.g., the pupil) are of higher resolution and thus can
be detected more reliably. Concretely, while the used Haar
classifiers can be trained for detecting both high or low res-
olution eye pairs, the subsequent pupil detection by ellipse
fitting only works reliable for high resolution eye pairs.
Our second user study concerns assessing the applicability of
gaze tracking to real-life use cases. Informed by results of the
first user study, we limit the evaluated use cases to scenarios
in which a low spatial accuracy suffices. The first use case
assesses the ability to detect if the user focuses on the device
or gazes besides it. Applications of this use case only having
limited accuracy requirements include device control or user
studies. The second use case evaluates the ability to use mo-
bile gaze tracking for supporting psychological field studies
diagnosing dyslexia.
Study Design
We base our study on the same setup as used in the first
study and uses our gaze tracking framework [2]. As the use
case evaluation requires temporal properties to be captured,
we now record a video stream. We further limit the lighting
conditions to use the wall light (LW) only. Each subject first
conducted the Focus on Device test followed by two tests as-
sessing the Reading Study use case (i.e., Line Test and Point
Test). We aligned the distance to 20cm by using the face
overlay of the first study. The alignment was only performed
once per subject in the beginning of each test. To evaluate the
spatial accuracy, we processed the recorded videos with the
Grad50 pupil detection and the Approx gaze detection algo-
rithm due to their good results in the first study.
For the Focus on Device test, we placed three red papers at
different angles to the left (20), to the right (45), and above
(60) the screen (see Figure 4). The placed papers indicate
gaze positions that a subject should focus upon request by
the test application. The application indicates this request by
displaying large visual arrows pointing to the paper (see Fig-
ure 3(a)). In addition, the test application requests the subject
to gaze back at the display by playing a sound. The used an-
gles allow us to compare for differences where subjects gazed
at the papers either with or without moving their head.
Thirteen subjects (11 male, 2 female) participated in the study
with an average age of 30.5 years (SD = 8.6 years). Only
two subjects also participated in the first study and four worn
glasses. To again mitigate bias, we selected persons with dif-
ferent eye colors: six persons with brown, two with green and
five with blue eyes.
Use Case: Focus on Device
Our first use case concerns detecting if users focus on the de-
vice or gaze beside it. Example applications include screen
unlocking (e.g., Samsung Smart Stay), HCI over multiple
screens [5], or the evaluation of visual attention in usability
tests [4]. We assume this use case to be feasible since it has
only minimal accuracy requirements.
Approach: Our heuristic is based on the measured error rate
and estimation accuracy. Gazing besides the screen will yield
high error rate since no eye can be detected in the picture.
Once the user gazes in the direction of the device, the error
rate will be low and gaze point estimation can be used to de-
tect if the screen was focused. As the limited accuracy of
700 px causes detection problems at the screen borders,
i.e., it is undecidable if a user looked on the screen, we vir-
tually increase the screen by an additional offset, such as the
measured accuracy.
Three subjects had to be removed as the gaze tracking failed
and thus no gaze points were located at the screen. For the
remaining subjects, we removed the first twenty frames for
each direction due to the reaction time required by the sub-
jects to gaze at the next line. On the remaining frames, we
then applied our heuristic with the offsets 0, 100, 200, 300
and 700 px. We did not exceed 700 px since the first user
study determined an average accuracy of about 705px.
Results: We show the percentage of gaze points that were de-
tected to be on screen for the four directions (gaze on screen,
gaze to left, to the right, or above the screen) in Figure 5. The
results show a high recognition rate when the subjects were
asked to gaze on the screen (see the bars on the left). In the
cases where the subjects were asked to gaze to the left, to the
right, and above the screen, some false positives are visible,
i.e., the on screen percentage is >0%. While the false pos-
itive rate increases with increasing offset, significant differ-
ences in the on screen percentage as well as non-overlapping
standard deviations suggest that both cases (i.e., user is (not)
Figure 4. Setup of study 2 Figure 5. Focus on device results
gazing on screen) can be differentiated. It is therefore possi-
ble to use EyeTab for estimating whether a user is gazing on
the device.
Use Case: Reading and Reading Training
Our last use case is motivated by an ongoing cooperation
with the Department of Psychiatry, Psychotherapy and Psy-
chosomatics at the RWTH Aachen University Hospital. This
use case aims at using mobile gaze tracking to support the
diagnosis and therapy of reading disorders, e.g., dyslexia.
Such disorders can be diagnosed by detecting irregular read-
ing pattern. Irregular pattern include non-linear reading be-
havior (horizontal regression) or inappropriate word fixation
lengths [23], each impacting how quickly and how fluently a
text is read. Both pattern are subject to this use case study.
Approach: We assess the ability of gaze tracking to detect
both reading irregularities in two tests. Each test is evaluated
by a dedicated heuristic as described below and by displaying
a dedicated test application. The detection concerns identify-
ing pattern rather than exact word or line positions.
Thus, instead of immediately focusing on detecting rather
complex reading pattern, we focus on detecting simplified
pattern that give first indications on whether the gaze tracking
approach can be used in reading studies. Concretely, our tests
display geometric dots rather than typefaces. This approach is
motivated by non-lexical reading tasks in which Landolt dots
are shown instead of typefaces to assess abnormal gaze pat-
tern without any influence of lexical information [10]. This
work showed that reading tasks without actual letters allows
dissociating the linguistic and orthographic brain networks
from pure gaze-orienting networks. They thus have the po-
tential to provide novel insights into non-linguistic factors on
reading disorders.
We further simplify the considered pattern by only detect-
ing linear reading pattern rather than normal reading pattern
which involve word fixations and saccade jumps. Concretely,
we ask subjects to follow a moving dot. This simplification
is motivated by reading studies showing that—for dyslexia
interventions like celeco [18]—machine-guided reading pat-
tern that ask readers to follow highlighted text segments on
lines, lead to an substantial increase of activation levels in the
visual word form area of the brain during reading tasks [9].
Another tool employed in predicting reading performance is
Rapid Automatized Naming [20], which uses matrices of let-
ters, numbers, colors and objects in random order.
Line 1 Line 2 Lin e 3 L ine 4 Line 5
Line Follow ing F ract ion in %
Ave rag e L ine Progression Fraction per Line
Offs et 0px
Offs et 10p x
Offs et 20p x
Figure 6. Line test
We then perform a second test to assess the ability to detect
word fixations. This test is motivated by observations that
found children with dyslexia to have more and longer fixa-
tions [14]. While the characterization of exact timings and
the corresponding neural pathways and networks are subject
to ongoing clinical research, the ability to detect basic fixa-
tions is arguably a basic requirement of successfully applying
CV based gaze tracking in mobile reading studies.
Thus, we rely on simplified but scientifically and clinically
relevant patterns instead of more complex natural reading pat-
terns as a prerequisite to later improvements. Challenges and
insufficiencies in detecting these simpler patterns would pre-
clude the detection of more complex patterns. Finally, this ap-
proach allows us to quantify, how well suited current mobile
gaze tracking approaches are for reading and reading train-
ing related tasks. As an additional use case, the detection of
reading pattern can improve context-aware interfaces by be-
ing able to differentiate if the user is i) reading (even if not
knowing the exact gaze position) or if the user is ii) staring
(gaze seems to be stuck).
Line Progression: Line Test
Approach: The heuristic for detecting if a line is linearly
read from left to right (horizontal line progression for latin
text) checks if two temporal successive gaze points x1, x2are
also spatial consecutively, i.e., x1x2. To account for noise
in the estimated gaze points, we allow (x1o)x2, where o
is an offset. The recognition rate increases with o, but also the
chance of missing non-linearities in the reading pattern (back
jumps to previously read words). The test procedure involved
displaying five horizontal lines where for each displayed line
the subjects were asked to follow a red dot moving from left
to right (see Figure 3(b)).
We discarded the first five captured video frames per line due
to the eye relocation time for switching to the next line. Since
all the lines are displayed, their positions are known and thus
a short eye relocation time suffices (i.e., 5 video frames). We
applied the explained heuristic and determined the percentage
of consecutive gaze points for which it holds that (x1o)
x2for o={0,10,20}px.
Results: We show the percentage of consecutive gaze points
satisfying our line progression heuristic for each tested line
Figure 7. Point test
and offset in Figure 6. The results highlight that line progres-
sion can be detected by using EyeTab. The achieved detec-
tion accuracy generally improves with increasing offset. Due
to the decrease of accuracy in the lower parts of the screen
(smaller angle), the recognition rate also decreases from the
upper to the lower lines. For example, at an offset of 20 px,
the recognition rate for line 1 (top) is 85% but only 67% for
line 5 (bottom).
Word Fixation: Point Test
Approach: To detect word fixations, we monitor all points
within a certain time period t. We then group these points into
a point cloud and represent it by its median. A word fixation
is detected if k% of the gaze points within a time tfall within
a radius raround the median. The quality of this heuristic
then depends on the radius ras well as the amount of points
kthat we require to be inside the circle. The test procedure
involved fixating a red dot in a 4×4grid in consecutive order,
i.e., from left to right and from the top to the bottom of the
screen (see Figure 3(c)).
We chose t= 3s, as the per item time for Rapid Automatized
Naming tasks for normal readers can reach 2.5s[30]. For
dyslexic children, this time can go up to an average time of
15sdepending on the item type [22].
Since the points were displayed in random order, we removed
the first 20 frames per point to allow the eye to relocate to
the next point. We then applied our heuristic with different
parameters for the coverage (i.e., CR ={80,85,90}%) and
the radius (i.e., R={100,150,200}px).
Results: We show the recognition rates of word fixations for
the different points and coverage parameters for a radius of
200 px in Figure 7. The results show that the recognition rate
can be as high as 77% in the top row (point 1 at 80% cov-
erage) and as low as 8% in the bottom row (point 16 at 90%
coverage). In general, increasing the coverage decreases the
recognition rate. Also, while points displayed at the top of
the screen benefit from a high accuracy yielding reasonable
recognition rates, points located in the lower half of the screen
suffer from an insufficient accuracy and only reach very low
recognition rates. Thus, detecting word fixations suffers from
the limited accuracy of EyeTab, in particular in the lower half
of the screen. If used, the tested words should only be dis-
played at the top of the screen where the angle between the
camera and the word position is sufficiently large.
The goal of this paper was to elucidate the current applicabil-
ity of computer vision (CV) based gaze tracking approaches
in mobile scenarios. Exemplified by the use of EyeTab, this
paper presented the first independent evaluation of mobile
gaze tracking performance and the first independent use case
driven evaluation of its applicability. By taking this appli-
cability perspective, we complemented a body of literature
focusing on the algorithmic development.
Current Applicability of Mobile Gaze Tracking
Our main finding is that mobile gaze tracking works but is
currently challenged by its low mean accuracy and high mean
error rate. The observed average accuracy of 700 px (15)
will render the approach inapplicable to many HCI use cases
that require a high spatial accuracy, e.g., evaluating visual at-
tention for interface design. This is further challenged by de-
creases in the accuracy with decreasing gaze to camera angle,
e.g., the observed accuracy in the top of the screen is much
higher in case of a bottom mounted camera (reverse landscape
viewing mode). Since our study was conducted in more ide-
alistic lab conditions, the observed distance sensitivity will
further challenge the everyday usage since users tend to hold
the device at varying distances and less ideal lighting settings.
Despite this limitation, we identified use cases in which the
approach is already applicable for low distances. The gaze
based detection on whether a user focuses on the device (ex-
cluding peripheral vision) has the potential to enrich usability
studies by indicating if a user could have seen an error con-
dition. It further allows use cases such as detecting active
screens and correctly positioning a mouse pointer on the ac-
tive screen in a multi-screen setup. Further, our reading use
case showed basic pattern recognition to work even on noisy
data. Concretely, it is indeed possible to some extent to de-
tect typical reading patterns: progressing from one word to
another within a line and word fixations. However, there is
fairly high uncertainty; tracking is fairly good on the top near
the camera, but it decreases towards the bottom.
Study Limitations
Our study was designed to provide baseline figures by as-
sessing the EyeTab performance in a controlled laboratory
setting. That is, we were able to control the ambient light
conditions as well as the viewing distance between the user
and the fixed tablet device. This design allowed us to provide
abaseline performance. In a more realistic real-life setting
(e.g., unfixed device, higher or varying viewing distances and
light conditions) the gaze tracking performance will be chal-
lenged and likely worse. Thus, use cases that do not work
well in controlled lab environments are unlikely to work in
more challenged real-life settings. While we remark that our
results are not directly transferable to these settings, we aim
to inform future studies by providing an intuition on which
use cases are likely to work in realistic settings. However, we
leave their evaluation of the gaze tracking performance in the
wild for future work since it requires a dedicated user study.
Likewise, our results are specific to EyeTab as promising
gaze tracking algorithm. We remark that other algorithms can
yield different results. Despite this limitation, we support the
evaluation of different and future gaze tracking algorithms by
i) providing evaluation methodologies and use cases that are
generically applicable to other algorithms, ii) by providing an
open source evaluation framework that can be extended with
additional gaze tracking algorithms to support future studies,
and iii) by providing a data set resulting from our user studies
that can be used in addition to evaluate other algorithms.
Open Gaze Tracking Database
The assessment of CV based gaze tracking approaches is cur-
rently limited to studies performed by the individual authors
(see e.g., [11, 15, 33]) and does not consider possible mobile
applications for which the approaches can still be used. Since
each study follows an individual design and the resulting ma-
terial is not available to the community, the comparison of
algorithmic performances is challenged and the evaluation of
new algorithms requires new studies to be conducted. Other
fields have progressed to release data collections to which
also other researchers can contribute individual data sets. Ex-
amples include the Live Video, Image Quality Database [24],
Internet measurement traces (see e.g., or CRAW-
DAD), and HCI Tagging Database [25].
To pave the way for improved CV based gaze tracking al-
gorithms, we publicly released our source data as database
available at [1] and invite external data sets to be submitted.
These data sets can serve as a baseline for developers that
use the existing data sets to evaluate their algorithms and ap-
proaches. The released data only includes subjects that signed
an additional consent form agreeing to such a release. The
form informed the subjects on possible privacy implications
of publicly releasing still pictures and video captures. Signing
this additional consent form was voluntary and not required
for participation in the study.
This work has been funded by the German Research Founda-
tion (DFG) within the Collaborative Research Center (CRC)
1053 – MAKI. We would like to thank Stefan Heim (Depart-
ment of Psychology, University Hospital RWTH Aachen Uni-
versity) for the fruitful discussions on the reading and reading
training use case. Last but not least, we thank the anony-
mous MobileHCI and external reviewers for their valuable
comments and suggestions to improve this manuscript.
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