Conference PaperPDF Available

Idiosyncratic repeatability of calibration errors during eye tracker calibration

Authors:

Abstract and Figures

Dynamic development of high quality cameras and algorithms processing eye movement signals entails growing interests in using them in various areas of human-computer interaction. Determining subjects which user is looking at or controlling the operation of computer processes can serve as examples of these areas. However, making eye movement signal valuable requires some preparatory steps to be taken. They belong to a process called calibration aiming at creating a model for mapping output delivered by an eye tracker to user’s gaze points. The quality of such model is assessed based on a calibration error defined as a difference between accurate data and this obtained from a model. The goal of the research presented in the paper was to analyse to what extent the calibration error depends on the specific participant’s features - it is repeatable – or to what extent it may be avoided during the recalibration. Additionally an influence of two calibration method a polynomial and an artificial neural network (ANN) on the final results were studied as well.
Content may be subject to copyright.
This is a pre-print. The paper will be published in the proceedings of the 7th IEEE International Conference on Human System Interaction and will be available in IEEE Xplore.
Idiosyncratic repeatability of calibration errors
during eye tracker calibration
Katarzyna Har ֒
zlak and Pawel Kasprowski and Mateusz Stasch
Institute of Informatics
Silesian University of Technology
Gliwice, Poland
Email: katarzyna.harezlak@polsl.pl, kasprowski@polsl.pl, mateusz.stasch@polsl.pl
Abstract—Dynamic development of high quality cameras and
algorithms processing eye movement signals entails growing
interests in using them in various areas of human-computer
interaction. Determining subjects which user is looking at or
controlling the operation of computer processes can serve as
examples of these areas. However, making eye movement signal
valuable requires some preparatory steps to be taken. They be-
long to a process called calibration aiming at creating a model for
mapping output delivered by an eye tracker to user’s gaze points.
The quality of such model is assessed based on a calibration error
defined as a difference between accurate data and this obtained
from a model. The goal of the research presented in the paper
was to analyse to what extent the calibration error depends on
the specific participant’s features - it is repeatable – or to what
extent it may be avoided during the recalibration. Additionally an
influence of two calibration method a polynomial and an artificial
neural network (ANN) on the final results were studied as well.
Keywords: eye movement, face recognition, data mining
I. INTRO DUC TI ON
Eyes are one of the most complicated human organs and
the analyses of eye movements may reveal a lot of information
about the human being. Eye movements may be used to
communicate with a computer environment, which may adapt
its behavior according to the user’s gaze directions [1]. The first
and most important element of such communication, which
highly influences all subsequent tasks, is properly realized
calibration process [2]. In most gaze-directed environments
it is crucial to precisely determine where a user is currently
looking. Although there exist eye trackers, which don’t need a
calibration, their setup is very complicated. Usage of a typical
eye tracker requires a prior calibration process, during which
an eye tracker raw output being an eye signal of an examined
person looking at a set of points with known positions (so
called Points of Regard of PoRs) is collected [3]. This output
is subsequently used to build a model for specifying user’s
gaze points for unknown areas. The quality of such model is
assessed based on a calibration error defined as a difference
between accurate data and this obtained from a model. If the
error is sufficiently low, an assumed interaction can start. In
other case - the error is too high - the user must be recalibrated
or, if it does not cause expected improvement, an interaction
has to be given up. The goal of the research presented in
the paper was to analyse to what extent the calibration error
depends on the specific participant’s features – it is repeatable
– or to what extent it may be avoided during the recalibration.
II. THE S TATE OF T HE ART
As it was stated in the previous section, properly performed
calibration is an essential part of every activity involving eye
movement signal. The calibration errors calculated during the
verification stage, are very often used to determine whether a
quality of data obtained during an experiment is sufficient.
But what should be done if this is not the case? Many
researchers use information about calibration results to remove
samples with low quality [4]. Surprisingly, the calibration
process has not attracted much attention in the eye movement
research. Developing a calibration scenario in most cases is
a responsibility of the eye tracker producers – researcher just
analyses output from manufacturer’s calibration procedure.
When preparing one’s own calibration scenario it is im-
portant to consider how many points – stimuli – to take into
account and in which locations they should be presented [5].
Another important choice, which can influence final results is
selecting a method used to map data provided by an eye tracker
to a user’s gaze point [3]. There can be various interpolation
functions, including polynomial ones with different degrees
and number of terms [6][7][8], artificial neural networks
[3][9][10] or Support Vector Regression [11] analyzed.
The results of calibration can also depend on a user [4]
and, surprisingly, may even be conditional upon the operator’s
experience [12]. Moreover, there are users for whom is difficult
to conduct an appropraite calibration process due to several
factors like: glasses, contact lenses, mascara or dropping lids
[12]. Most of them may be easily avoided (e.g. by getting
off glasses or removing mascara). However, it is not obvious
whether there are any physiological or behavioral features
that make the specific person constantly reluctant to a proper
calibration. This paper tries to study that problem, comparing
calibration information collected during three different sessions
involving the same group of the participants.
III. RES EA RCH E NV I RON MEN T
The experimental setup of the tracking system used in
the research is shown in the figures 2 and 3. The main its
component was a VOG head-mounted eye tracker developed
with single CMOS camera with USB 2.0 interface (Logitech
QuickCam Express) with 352x288 sensor and lens with IR-
Pass filter. The camera was mounted on an arm attached to a
head and was pointing at the right eye. The eye was illuminated
with a single IR LED, placed off the axis of the eye that
causes the “dark pupil” effect, which was useful during pupil
This is a pre-print. The paper will be published in the proceedings of the 7th IEEE International Conference on Human System Interaction and will be available in IEEE Xplore.
detection. The image obtained from the camera is converted to
grayscale. Subsequently it is tresholded to change the darkest
points of the image into white ones (figure 1). For such
processed image the contour detection algorithm is applied to
find the smallest convex polygon surrounding the white shape.
The center of gravity of this polygon is a estimated center of a
pupil.The system generates 20 - 25 measurements of the centre
of the pupil per second.
(a) Grayscale image (b) Thresholded image
Fig. 1: Pupil detection algorithm
The calibration was done on a 1280x1024 (370mm x
295mm) flat screen. The eye-screen distance was equal to
500mm and vertical and horizontal gaze angles were 40and
32respectively. To avoid movements, the head was stabilized
using a chin rest.
The experiments were repeated three times using a set of 29
points distributed over the screen as presented in the figure 4.
In each session, points were displayed in the same, predefined
order. Each point was displayed for 3618 msec. and it was
pulsating in order to keep the user focused on it. The time
interval between sessions for one user was at least three weeks
to avoid the learning effect - when user learns the order of
the points and is able to anticipate the next point position.
All session took place in the same day in a week, the same
time during a day and in the same room. Therefore, it can be
said that the conditions of the experiments were comparative.
Altogether 24 participants took part in the experiments. Some
of them were involved only in two of three sessions, but most
of users participated in all of them. Before each experiment,
participants were informed about the general purpose of the
experiment after which they signed a consent form. As it was
said, the participants numbers differed between sessions and
not all of them took part in all sessions. This is why the sets
of the participants for session 1 and 2, 1 and 3 and 2 and 3
had to be made uniform. Their final numbers are presented in
table I.
TABLE I: Number of the participants involved in particular
sessions
Sessions Participants number
1 and 2 20
1 and 3 20
2 and 3 18
The participants’ eye positions reflecting points of regard
(PoR) presented on a screen, acquired during particular ses-
sions, were used to define two types of models mapping an
eye position of a participant to PoRs for unknown samples
(in the same session). The first of them was based on a
Fig. 2: The architecture of the system
Fig. 3: Photo of the measurement setup
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1
2
3
4
56
78
9/29
10
11 12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Fig. 4: Layout and order of calibration points
commonly used the second order polynomial function [6][7],
for which values of Ax...Exand Ay...Eyparameters (1)
were calculated using a classic Levenberg-Marquardt optimizer
[13].
xs=Axx2
e+Bxy2
e+Cxxe+Dxye+Ex(1)
ys=Ayx2
e+Byy2
e+Cyxe+Dyye+Ey
where xsand ysare estimated gaze coordinates on a screen.
The second type of mapping was performed using an
artificial neural network (ANN). An activation network with
sigmoid function as an activation function was used. The net-
work was trained using the Back Propagation algorithm with
This is a pre-print. The paper will be published in the proceedings of the 7th IEEE International Conference on Human System Interaction and will be available in IEEE Xplore.
normalized samples recorded during a session. Configuration
of the network consisted of two neurons in the input layer, 10
neurons in one hidden layer and two neurons as the output.
The network was trained until the total train error was lower
than 0.1. ANN has been already used in several eye tracing
applications [10][9]. In the first step of the research the models
built using these functions were based on all points presented
to the participants - for each session independently. These
models were subsequently tested using the same set of points.
Their quality was verified by computing the distance between
accurate positions of the displayed points and their locations
obtained from a specific model. This factor, expressed in
degrees, was calculated according to the equitation 2.
Edeg =1
nX
i
p(xibxi)2+ (yibyi)2(2)
where yi,xirepresent observed values, bxi;byirepresent
value calculated by a model.It must be emphasized that it
takes some time for an eye to react to a stimulus position
change to fixate on another position. Such occurrence is
called saccadic latency and lasts approximately 100-300 msec
[14]. During earlier experiments (not published yet), it was
calculated that the safest range of measurements to include
for further studies is obtained between 700 msec. and 1800
msec. after the stimulus position changed. Therefore, only
these measurements were taken into account in both training
and validation phases.
IV. RES ULTS COR RE L ATION
EDeg values evaluated in the step described above were
used to assess the codependency of the results acquired in
particular sessions. Based on its values the correlation coeffi-
cients between session 1 and 2, session 1 and 3 and session
2 and 3 were calculated. Analyzing outcomes presented in
table II, high correlation between results obtained in the first
and the second as well as the first and the third sessions can
be noticed, especially for models based on the polynomial
function. Similarly, high correlation can be observed for the
first and the third session in case of the ANN method. The
correlation of results calculated by this method in case of the
first and the second sessions along with results obtained by
the polynomial function in case of the second and the third
sessions can be considered as meaningful. Only the results
related to the ANN method for the second and the third
sessions turned to be uncorrelated.
TABLE II: Values of the correlation coefficients for particular
sessions
Sessions Polynomial
method
ANN
method
1 and 2 0,626345 0,487624
1 and 3 0,673711 0,594534
2 and 3 0,354834 0,125152
Statistical significance of the computed results was studied,
taking the lack of correlation as a hypothesis H0 and p=0,05
as the significance level. The conduced tests confirmed most
of the outcomes, by rejecting hypothesis H0 in 4 out of 6
instances (Table III). In case of session 2 and 3 and the
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
9
10
11 12
13
Fig. 5: Layout of the set 5-1
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1 5
78
9
10
11 12
13
Fig. 6: Layout of the set 9-1
polynomial function, the hypothesis H0 could not be rejected,
yet it is worth emphasizing that the achieved value was close
to the boundary level.
TABLE III: The results of the statistical tests of the calibration
errors correlation
P-value
Sessions Polynomial
method
ANN
method
1 and 2 0,000779 0,017528
1 and 3 0,000209 0,001645
2 and 3 0,069114 0,398373
V. RESU LTS REPE ATABI LIT Y OV ER S E TS OF P OI NTS
The promising results, obtained in the previously described
studies, were encouraging to more detailed analyses of cap-
tured samples. Therefore, in a collection of all 29 points
used in experiments, sets differing in a number and layouts
of points were distinguished. There were 61 sets defined and
sets presented in figure 5 and 6 can serve as examples. Due
to limited space, the detailed description of all sets is not
presented here.
This is a pre-print. The paper will be published in the proceedings of the 7th IEEE International Conference on Human System Interaction and will be available in IEEE Xplore.
0%
20%
40%
60%
80%
100%
1 4 5 8 9 12 14 16 17 19 20 21 22 23 24 25 26 27 28 29 30 32 34
Percent of repetitive sets
Participant number
Polynomial function
Fig. 7: Percentage of repetitive sets for various participants -
The polynomial function
Samples related to a particular set and a particular par-
ticipant were used to define calibration models taking the
polynomial (equitation 1) and ANN methods into account. All
models were checked using 16 testing points from the same
session.
Models assessment, once again, was performed using EDeg
error values (equitation 1). These values were studied to check
if the best results, defined as ones with the lowest error degree,
in all sessions are related to the same sets of points. Because
of the fact that results of some sets were very close to each
other, it was assumed that they would be treated equivalently. It
regarded the results, for which the difference between ordered
ascending EDeg values were lower than 0.5 degree. Such rule
was applied for all participants, all sessions and both methods
described earlier. Selected sets of points will be further referred
as the best results set. The first finding of the aforementioned
studies was an evaluation of participants number, in case of
which the same set of points was found in more than one of
the best results sets. It amounted to 88% of the participants
in case of the polynomial function usage and 77% for the
second of functions. Subsequently, a percentage ratio of a
number of repeated elements to a number of distinct elements
in the best results sets obtained for all three sessions was
analyzed. This analysis was conducted for each participant
and for each regression function independently. The obtained
results are presented in the figures 7 and 8 for the polynomial
and the ANN functions respectively. The values seen on the
OX axis represent a number associated with a participant,
while the OY axis represents the percentage of repetitive sets
for a given participant. It can be observed that in case of
polynomial function results differ significantly: the minimal
ratio representing a recurrence of sets is 10% for participant
marked with number 22 and its maximal value 84% is related
to person with number 5, averaging on 40%.
Similar results, although a little bit worse, were obtained
for the second of functions. Minimal value amounts to 11%
for a participant number 1, maximal value - 78% is related to
person number 5 and the average value is 33%.
Summarizing the obtained results, they were divided into
four groups taking calculated ratio as a criterion. The partic-
ipants with ratio falling between 0 and 20% constituted the
first group. Similarly, the next four groups were defined for
0%
20%
40%
60%
80%
100%
1 2 3 4 5 9 16 17 20 21 23 24 25 26 27 28 29 30 32 34
Percent of repetitive sets
Participant number
ANN function
Fig. 8: Percentage of repetitive sets for various participants -
The ANN function
participants related to ratios belonging to (20% , 40%], (40%,
60%] and (60%, 80%], (80%, 100%] partitions respectively.
Data presented in table IV indicates that in case of polynomial
function, the highest percentage of the participants was related
to ratio belonging to scopes of 20% - 40% and 40% - 60%.
of the sets repetitiveness. The second of functions provided
outcomes, which placed the majority of the participants in the
ratio partitions defined by scopes of 0% - 20% and of 20%
- 40%, which is worse results in a comparison with the first
function.
TABLE IV: Percentage of users with repetitive results in a
given scope
Percentage of users with a given scope of repetitiveness
Partition scope Polynomial
method
ANN
method
20% 17% 35%
>20% and 40% 35% 40%
>40% and 60% 30% 15%
>60% and 80% 13% 10%
>80% 4% 0%
VI. AN ALYSI S OF T H E RE SULTS
To start the analysis of the results the main questions asked
at the beginning of the paper: (1) to what extent the calibration
error depends on the specific participant’s features or (2) to
what extent it may be avoided during the recalibration - have
to be answered.
Experiments leading to gain such knowledge included a
triple calibration procedure. The collected data was used to
build calibration models using two functions – the polynomial
with a second order and the ANN one. The calibration accuracy
was verified using error values equal to a degree distance
between an accurate and a calculated position of a point.
The values were firstly used to check the correlation of data
originating from various sessions. As it was presented earlier,
the estimated coefficients, confirmed by statistical tests, indi-
cated the moderate or significant correlation between sets of
values obtained for first and second sessions and first and third
ones. These results regarded both of the analyzed methods.
Conclusions, which can be drawn based on these studies, are
twofold. On one hand they revealed relation between calibra-
tion errors. On the other hand, the strength of that correlation,
This is a pre-print. The paper will be published in the proceedings of the 7th IEEE International Conference on Human System Interaction and will be available in IEEE Xplore.
not reaching highest possible values, in conjunction with weak
correlation between outcomes obtained for second and third
sessions, shows that there are still opportunities to improve an
eye movement signal acquisition. This can be achieved, for
example, by triggering recalibration process.
Such ambiguity of the research findings encouraged us to
conduct further analysis. It was interesting to find out the
extent to which the general results are reflected in data of
particular users. To make analysis more detailed the set of 29
points used in the experiment was divided in smaller groups
differing in numbers and layouts of their elements. The aim of
this detailed studies was to check how repetitive is registered
eye movement signal when various calibration scenarios were
taken into account. The attention was focused only on this
scenarios, for which values of calibration error were equal or
lower than 0.5 degree.
The analysis was conducted from two points of view. At
first, it was checked how many participants obtained con-
sidered calibration error for the same scenarios in different
sessions of the experiment, yet these scenarios did not neces-
sarily have to be the same for various participants. Evaluated
percentage of such users for both studied methods (polynomial
and ANN ones) was quite high and amounted 88% and
77% respectively. It can indicate that human’s eye movement
signal, if registered with appropriately adjusted scenario, can
make calibration procedure highly repetitive. An influence of a
chosen calibration method has to be emphasized as well. After
that, recalibration can make collected data more reliable.
These conclusions seem to be confirmed by the analysis
done from the second point of view. It is represented by
studies of the percentage ratio describing, independently for
each participant, number of repeated sets in relation to the
number of distinct elements appearing in three sessions. These
studies revealed high diversity of the ratio, which varied from
10% to 80% for both methods, with an average value of 40%
for the polynomial function and 33% for the ANN one. Such
unstable results indicate that any of the calibration processes
was influenced by some problems and some studies have to be
done to eliminated these obstacles. Providing the knowledge
in how many cases recalibration should be done was the aim
of the last type of analysis conducted during the research. As
it was presented in table IV majority of results representing
calibration error repetitiveness were, for polynomial function,
classified in the range from 0% to 60%. For these group
of users, it can be expected that recalibration process for
primarily obtained high calibration errors, can improve quality
of data gathered during subsequent measurements of an eye
movement signal. Repetitiveness higher than 60% can suggest
that the value of a calibration error can recur, which is a good
information when it is low and bad news in other case.
Considering the result for the ANN methods it can be
reasoned that this function, for the eye tracker used in the
experiment, provided less repetitive results. This makes it a
less stable method entailing, with higher probability, triggering
a recalibration process more frequently.
VII. SU MM ARY
The research presented in the paper aimed at determining
whether a repeated calibration process is characterized by the
same accuracy in each of its occurrences. This accuracy is
expressed by a calibration error being the distance between
actual position and measured user’s gaze point. Values of this
error were used to compare the quality of data acquired for
the participants taking part in three sessions of the experiment.
To test various scenarios the different sets of points and two
calibration methods were analyzed.
The main goal of these activities was to check whether
a participant reproduces the stable data over various sessions
or collected samples vary between trials. Repetitive results of
the calibration error indicate that one calibration attempt is
sufficient to decide whether particular user should take part in
the subsequent tasks of the experiment or not. In the second
case retriggering calibration process can lead to improvement
of calibration results. However this recalibration should not be
repeated infinitely. It is not a big problem when eye movements
are collected under specialized operator supervision. In case of
too high error values an operator may suggest ways to improve
results such as, for example, removing mascara. But the results
presented in this paper are very important when developing
human computer interfaces that are intended to be used without
any supervision. Improperly developed interface may try to
constantly recalibrate user for which it is just impossible to
obtain correct results. In such case, some overlay algorithms
should be proposed.
To conclude the research, it is important to wonder if the
studies provided answers for questions asked at the beginning
of the paper. The response, which can be given is not as clear as
one would like to obtain. On one hand, they proved that there
are some people, who are able to keep their eye movement
signal on almost the same level when an appropriate scenario
and method is used. It simplifies making decisions whether a
user should or not be excluded for experiment. On the other
hand, the majority of the participants were related to less, but
still meaningful, repetitiveness of the results. For them it can
be useful to restart calibration process with the same setup or
with a changed calibration scenario.
Finally, it has to be emphasised that during the research
an eye movement signal was collected using a simple eye-
tracking environment, accessible for ordinary users. It can be
expected that usage of higher quality eye-trackers can provide
better results than these presented in the paper. Yet, because
of the cost necessary to bear, they are inaccessible for many
users and for those people outcomes presented in the paper
may seem to be useful.
Findings of the described research indicate that consecutive
studies should be realized. The idea of a recalibration algo-
rithm has to be elaborated. Additionally, existence of people
for whom collaboration with eye trackers is difficult, entitles
a necessity of searching for reasons and methods, which solve
this problem.
Another possible extension of the current work may be
based on the fact that there were participants, representing the
fourth part of the population, for whom repeatability between
sessions reached 60% - 100%. This finding could be used as
the indicator that eye movements are personally distinctive and
- what is even more important - repeatable. However, all of
the intended studies require involving more participants.
This is a pre-print. The paper will be published in the proceedings of the 7th IEEE International Conference on Human System Interaction and will be available in IEEE Xplore.
REF ERE NC ES
[1] R. J. K. Jacob, “The use of eye movements in human-computer
interaction techniques: What you look at is what you get,ACM
Transactions on Information Systems, vol. 9, pp. 152–169, 1991.
[2] A. T. Duchowski, “A breadth-first survey of eye-tracking applications,
Behavior Research Methods, Instruments, & Computers, vol. 34, no. 4,
pp. 455–470, 2002.
[3] P. Kasprowski, K. Har֒
zlak, and M. Stasch, “Guidelines for the eye
tracker calibration using points of regard,Information Technologies in
Biomedicine, 2014.
[4] K. Holmqvist, M. Nystr¨om, R. Andersson, R. Dewhurst, H. Jarodzka,
and J. Van de Weijer, Eye tracking: A comprehensive guide to methods
and measures. Oxford University Press, 2011.
[5] X. Brolly and J. Mulligan, “Implicit calibration of a remote gaze
tracker,” in Computer Vision and Pattern Recognition Workshop, 2004.
CVPRW ’04. Conference on, 2004, pp. 134–134.
[6] P. Blignaut and D. Wium, “The effect of mapping function on the
accuracy of a video-based eye tracker,” in Proceedings of the 2013
Conference on Eye Tracking South Africa, ser. ETSA ’13. New York,
NY, USA: ACM, 2013, pp. 39–46.
[7] J. J. Cerrolaza, A. Villanueva, and R. Cabeza, “Taxonomic study of
polynomial regressions applied to the calibration of video-oculographic
systems,” in Proceedings of the 2008 Symposium on Eye Tracking
Research & Applications, ser. ETRA ’08. New York, NY, USA: ACM,
2008, pp. 259–266.
[8] N. Ramanauskas, “Calibration of video-oculographical eye-tracking
system,” Electronics and Electrical Engineering, vol. 8, no. 72, pp.
65–68, 2006.
[9] Z. Zhu and Q. Ji, “Eye and gaze tracking for interactive graphic display,
Machine Vision and Applications, vol. 15, no. 3, pp. 139–148, 2004.
[10] K. Essig, M. Pomplun, and H. Ritter, “A neural network for 3d gaze
recording with binocular eye trackers.” IJPEDS, vol. 21, no. 2, pp. 79–
95, 2006.
[11] A. J. Smola and B. Sch¨olkopf, “A tutorial on support vector regression,
Statistics and computing, vol. 14, no. 3, pp. 199–222, 2004.
[12] M. Nystr¨om, R. Andersson, K. Holmqvist, and J. van de Weijer, “The
influence of calibration method and eye physiology on eyetracking data
quality,” Behavior research methods, vol. 45, no. 1, pp. 272–288, 2013.
[13] J. J. Mor´e, “The levenberg-marquardt algorithm: implementation and
theory,” in Numerical analysis. Springer, 1978, pp. 105–116.
[14] J. H. Darrien, K. Herd, L.-J. Starling, J. R. Rosenberg, and J. D.
Morrison, “An analysis of the dependence of saccadic latency on
target position and target characteristics in human subjects,BMC
neuroscience, vol. 2, no. 1, p. 13, 2001.
[15] C. H. Morimoto and M. R. M. Mimica, “Eye gaze tracking techniques
for interactive applications,” Comput. Vis. Image Underst., vol. 98, no. 1,
pp. 4–24, Apr. 2005.
[16] A. Villanueva and R. Cabeza, “Models for gaze tracking systems,
Journal on Image and Video Processing, vol. 2007, no. 3, p. 4, 2007.
... Based on this ground truth information, a calibration model is created by the eye tracker's software. It is well known that these calibration models are idiosyncratic [Harężlak et al. 2014], which means that the model calculated for one person may be used by that person again. There is, of course, some degradation of accuracy, but it may sometimes be acceptable. ...
Conference Paper
The purpose of the paper is to test the possibility of identifying people based on the input they provide to an eye tracker during the calibration process. The most popular eye trackers require the calibration before their first usage. The calibration model that is built can recalculate the subsequent eye tracker's output to genuine gaze points. It is well known that the model is idiosyncratic (individual for the person). The calibration should be repeated every time the person uses the eye tracker. However, there is evidence that the models created for the same persons may be reused by them (but obviously with some loss of accuracy). The general idea investigated in this paper is that if we take an uncalibrated eye tracker's output and compare it with the genuine gaze points, the errors will be repeatable for the same person. We tested this idea using three datasets with an eye tracker signal recorded for 52 users. The results are promising as the accuracy of identification (1 of N) for the datasets varied from 49% to 71%.
... The experiment presented in this paper shows that it is possible to use an uncalibrated eye tracker signal to verify users' identity claims. It utilizes the known fact that the calibration model is idiosyncratic (depends on a person) [Harezlak et al. 2014]. That is why it may be assumed that the eye tracker calibrated for one person will exhibit higher gaze point location errors when another person uses it. ...
Conference Paper
Eye movement-based biometric has been developed for over 15 years, but for now - to the authors' knowledge - no commercial applications utilize this modality. There are many reasons for this, starting from still low accuracy and ending with the problematic setup. One of the essential elements of this setup is the calibration , as nearly every eye tracker needs to be calibrated before its first usage. This procedure makes any authentication based on eye movement a cumbersome and lengthy process. The main idea of the research presented in this paper is to perform authentication based on a signal from a cheap remote eye tracker but - contrary to the previous studies - without any calibration of the device. The uncalibrated signal obtained from the eye tracker is used directly, which significantly simplifies the enrollment process. The experiment presented in the paper aims at protection from a so-called "lunchtime attack" when an unauthorized person starts using a computer, taking advantage of the absence of the legitimate user. We show that such an impostor may be detected with an analysis of the signal obtained from the eye tracker when the user clicks with a mouse objects on a screen. The method utilizes the assumptions that: (1) users usually look at the point they click, and (2) an uncalibrated eye tracker signal is different for different users. It has been shown that after the analysis of nine subsequent clicks, the method is able to achieve the Equal Error Rate lower than 15% and may be treated as a valuable and difficult to counterfeit supplement to classic face recognition and password-based computer protection methods.
... The center of gravity of this region is an estimated center of the pupil (Hansen and Ji, 2010). Works in which the found region is changed into white one may also be found (Goni et al., 2004;Harezlak et al., 2014a). Sometimes when an image is characterized by low resolution and low contrast the normalization of the image histogram is applied. ...
Article
The performance and quality of medical procedures and treatments are inextricably linked to technological development. The application of more advanced techniques provides the opportunity to gain wider knowledge and deeper understanding of the human body and mind functioning. The eye tracking methods used to register eye movement to find the direction and targets of a person's gaze are well in line with the nature of the topic. By providing methods for capturing and processing images of the eye it has become possible not only to reveal abnormalities in eye functioning but also to conduct cognitive studies focused on learning about peoples’ emotions and intentions. The usefulness of the application of eye tracking technology in medicine was proved in many research studies. The aim of this paper is to give an insight into those studies and the way they utilize eye imaging in medical applications. These studies were differentiated taking their purpose and experimental paradigms into account. Additionally, methods for eye movement visualization and metrics for its quantifying were presented. Apart from presenting the state of the art, the aim of the paper was also to point out possible applications of eye tracking in medicine that have not been exhaustively investigated yet, and are going to be a perspective long-term direction of research.
Article
Biometric Systems Now a Days are playing a vital role in the whole word. Biometric Identification is being carried out in all the industrics, Institutions and in all the concerns wherr we are in need of the security and heavy monitoring. There are various traints considered for identifying and Authenticating the individual persons, they may include the behaviourial and also the physiological traits. Eye tracking is the process of measuring either the point of gaze (where one is looking) or the motion of an eye relative to the head. An eye tracker is a device for measuring eye positions and eye movement. Eye trackers are used in research on the visual system. This paper focuses on the identification with low frequency Eye Tracker. he most widely used current designs are video-based eye trackers. A camera focuses on one or both eyes and records their movement as the viewer looks at some kind of stimulus. Most modern eye-trackers use the center of the pupil and infrared / near-infrared non-collimated light to create corneal reflections (CR). The vector between the pupil center and the corneal reflections can be used to compute the point of regard on surface or the gaze direction. A simple calibration procedure of the individual is usually needed before using the eye tracker. This paper presents a review of the related works in the field as well as a general classification of different identification types. We stated a formal description of a saccade and an analyzed fragment of the gaze trajectory containing a saccade, which is based on the finite differences method, and revealed features of the eye movements that can be used for the identification purpose. Two classifiers are proposed and compared based on the experimental results obtained for them and we examined whether the delivery of an attentional bias modification (ABM) procedure in the presleep period could produce transient benefits for sleep-disturbed individuals by reducing presleep cognitive arousal and improving ease of sleep onset. These results suggest that delivery of ABM can attenuate cognitive arousal and sleep onset latency and highlights the possibility that targeted delivery of ABM could deliver real-world benefits for sleep-disturbed individuals.
Article
Biometric identification is one of the most secure existing identification methods. Algorithms based on eye tracking techniques are being studied now by many authors as eye trackers had become more accessible during the last few years. This paper is related to a biometric identification approach and a problem of providing accurate identification results by using low-frequency eye tracker. This paper presents a review of the related works in the field as well as a general classification of different identification types. We stated a formal description of a saccade and an analyzed fragment of the gaze trajectory containing a saccade, which is based on the finite differences method, and revealed features of the eye movements that can be used for the identification purpose. Two classifiers are proposed and compared based on the experimental results obtained for them.
Conference Paper
In this paper a simple method of orientational path planning for commonly used 6R manipulators is presented. The chosen criterion is to minimize changes of values of manipulator's joints. For a given set of locations a proper orientation of end-effector has to be computed. The three Euler angles (α, β, γ) have been replaced with one angle (δ). In order to reduce the number of variables describing the orientation an algorithm which derives a plane equation based on three neighboring points is proposed. The angle between normal vector to the plane and one of the vectors describing the coordinate system attached to the end-effector is equal to zero. The choice of right vector is based on end-effector construction and operator's knowledge. If three neighboring points are collinear, algorithm creates the plane based on two points of given path and the origin of base coordinate system. If these four points are collinear, the plane is created with use of the points projection to the base X0Y0 plane or plane parallel to X0Y0. In order to ensure the path between considered points an interpolation is made. A comparison of Hermite polynomials and linear segments with parabolic blends is presented. The angle δ describes the rotation angle of coordinate system at start of the first iteration. The angle change is chosen in order to minimize the joint values changes.
Conference Paper
Full-text available
Eye movement data may be used for many various purposes. In most cases it is utilized to estimate a gaze point - that is a place where a person is looking at. Most devices registering eye movements, called eye trackers, return information about relative position of an eye, without information about a gaze point. To obtain this information, it is necessary to build a function that maps output from an eye tracker to horizontal and vertical coordinates of a gaze point. Usually eye movement is recorded when a user tracks a group of stimuli being a set of points displayed on a screen. The paper analyzes possible scenarios of such stimulus presentation and discuses an influence of usage of five different regression functions and two different head mounted eye trackers on the results.
Conference Paper
Full-text available
In a video-based eye tracker the pupil-glint vector changes as the eyes move. Using an appropriate model, the pupil-glint vector can be mapped to coordinates of the point of regard (PoR). Using a simple hardware configuration with one camera and one infrared source, the accuracy that can be achieved with various mapping models is compared with one another. No single model proved to be the best for all participants. It was also found that the arrangement and number of calibration targets has a significant effect on the accuracy that can be achieved with the said hardware configuration. A mapping model is proposed that provides reasonably good results for all participants provided that a calibration set with at least 8 targets is used. It was shown that although a large number of calibration targets (18) provide slightly better accuracy than a smaller number of targets (8), the improvement might not be worth the extra effort during a calibration session.
Article
Full-text available
Holmqvist, K., Nyström, N., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Weijer, J. (Eds.) (2011). Eye tracking: a comprehensive guide to methods and measures, Oxford, UK: Oxford University Press.
Article
Full-text available
Recording eye movement data with high quality is often a prerequisite for producing valid and replicable results and for drawing well-founded conclusions about the oculomotor system. Today, many aspects of data quality are often informally discussed among researchers but are very seldom measured, quantified, and reported. Here we systematically investigated how the calibration method, aspects of participants' eye physiologies, the influences of recording time and gaze direction, and the experience of operators affect the quality of data recorded with a common tower-mounted, video-based eyetracker. We quantified accuracy, precision, and the amount of valid data, and found an increase in data quality when the participant indicated that he or she was looking at a calibration target, as compared to leaving this decision to the operator or the eyetracker software. Moreover, our results provide statistical evidence of how factors such as glasses, contact lenses, eye color, eyelashes, and mascara influence data quality. This method and the results provide eye movement researchers with an understanding of what is required to record high-quality data, as well as providing manufacturers with the knowledge to build better eyetrackers.
Article
This paper presents a review of eye gaze tracking technology and focuses on recent advancements that might facilitate its use in general computer applications. Early eye gaze tracking devices were appropriate for scientific exploration in controlled environments. Although it has been thought for long that they have the potential to become important computer input devices as well, the technology still lacks important usability requirements that hinders its applicability. We present a detailed description of the pupil–corneal reflection technique due to its claimed usability advantages, and show that this method is still not quite appropriate for general interactive applications. Finally, we present several recent techniques for remote eye gaze tracking with improved usability. These new solutions simplify or eliminate the calibration procedure and allow free head motion.
Conference Paper
Of gaze tracking techniques, video-oculography (VOG) is one of the most attractive because of its versatility and simplicity. VOG systems based on general purpose mapping methods use simple polynomial expressions to estimate a user's point of regard. Although the behaviour of such systems is generally acceptable, a detailed study of the calibration process is needed to facilitate progress in improving accuracy and tolerance to user head movement. To date, there has been no thorough comparative study of how mapping equations affect final system response. After developing a taxonomic classification of calibration functions, we examine over 400,000 models and evaluate the validity of several conventional assumptions. The rigorous experimental procedure employed enabled us to optimize the calibration process for a real VOG gaze tracking system and, thereby, halve the calibration time without detrimental effect on accuracy or tolerance to head movement.