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Eye Movements in Biometrics

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The paper presents a brand new technique of performing human identification which is based on eye movements characteristic. Using this method, the system measures human’s eyes reaction for visual stimulation. The eyes of the person who is being identified follow the point on the computer screen and eye movement tracking system is used to collect information about eye movements during the experiment. The first experiments showed that it was possible to identify people by means of that method. The method scrutinized here has several significant advantages. It compiles behavioral and physiological aspects and therefore it is difficult to counterfeit and at the same time it is easy to perform. Moreover, it is possible to combine it with other camera-based techniques like iris or face recognition.
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Eye movements in biometrics
Pawel Kasprowski1, Józef Ober1,2
1Institute of Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland
2Institute of Theoretical and Applied Informatics, Polish Academy of Science, Gliwice,
Poland
kasprowski@polsl.pl
Abstract. The paper presents a brand new technique of performing human
identification which is based on eye movements characteristic. Using this
method, the system measures human’s eyes reaction for visual stimulation. The
eyes of the person who is being identified follow the point on the computer
screen and eye movement tracking system is used to collect information about
eye movements during the experiment. The first experiments showed that it was
possible to identify people by means of that method. The method scrutinized
here has several significant advantages. It compiles behavioral and
physiological aspects and therefore it is difficult to counterfeit and at the same
time it is easy to perform. Moreover, it is possible to combine it with other
camera-based techniques like iris or face recognition.
1 Introduction
Eyes are one of the most important human organs. There is a common saying that
eyes are ‘windows to our soul’. In fact eyes are the main ‘interface’ between
environment and human brain. Therefore, it is not a surprise that the system which
deals with human vision is physiologically and neurologically complicated.
Using eyes to perform human identification in biometric methods has a long
tradition including well established iris pattern recognition algorithms [1] and retina
scanning. However, as far as the authors of the paper know, there are not any
researches concerning identification based on eye movement characteristic. It is a bit
surprising because that method has several important advantages.
Firstly, it compiles physiological (muscles) and behavioral (brain) aspects. The
most popular biometric methods like fingerprint verification or iris recognition are
based mostly on physiological properties of human body. Therefore, what is needed
for proper identification is only a “body” of a person who is to be identified. It makes
it possible to identify an unconscious or - in some methods - even a dead person.
Moreover, physiological properties may be forged. Preparing models of a finger or
even retina (using special holograms) is technically possible. As eye movement based
identification uses information which is produced mostly by brain (so far impossible
to be imitated), forging this kind of information seems to be much more difficult.
Although it has not been studied in the present paper, it seems possible to perform
a covert identification, i.e. identification of a person unaware of that process (for
instance using hidden cameras).
This is a pre-print.
The original version was published by Springer,
Lecture Notes in Computer Science vol 3087,
Book title: Biometric Authentication, pp 248-258,
DOI 10.1007/978-3-540-25976-3_23
Last but not least, there are many easy to use eye tracking devices nowadays, so
performing identification by means of that technique is not very expensive. For
instance a very fast and accurate OBER2 [2] eye tracking system was used in the
present work. It measures eye movements with a very high precision using infrared
reflection and the production costs are comparable to fingerprint scanners.
2 Physiology of eye movements
When individual looks at an object, the image of the object is projected on to the
retina, which is composed of light-sensitive cells that convert light into signals which
in turn can be transmitted to brain via the optic nerve. The density (or distribution) of
this light-sensitive cells on retina is uneven, with denser clustering at the centre of the
retina rather than at the periphery. Such clustering causes the acuity of vision to vary,
with the most detailed vision available when the object of interest falls on the centre
of the retina. This area is called yellow dot or fovea and covers about two degrees of
visual angle. Outside this region visual acuity rapidly decreases. Eye movements are
made to reorient the eye so that the object of interest falls upon the fovea and the
highest level of detail can be extracted [3].
That is why it is possible to define a ‘gaze point’ an exact point a person is
looking at in a given moment of time. When eyes are looking at something for a
period of time this state of the eye is called a fixation. During that time the image
which is projected on the fovea is analyzed by the brain. The standard fixation lasts
for about 200-300 ms, but of course it depends on the complexity of an image which
is observed. After the fixation, eyes move rapidly to another gaze point another
fixation. This rapid movement is termed a saccade. Saccades differ in longitude, yet
always are very fast.
To enable brain to acquire image in real time, the system which controls eye
movements (termed oculomotor system) has to be very fast and accurate. It is built of
six extraocular muscles which act as three agonist/antagonist pairs. Eyes are
controlled directly by the brain and its movements are the fastest reactions for
changing environment.
3 Previous researches concerning eye movements
Eye movements are essential to visual perception [4], so it is not a surprise that
there are a lot of researches on our vision. Most of them are concerned with
neurobiological and psychological aspects of vision.
One of the first scientists who emphasized the importance of eye movements in
vision and perception was Descartes (1596-1650). First known researches were made
by French ophthalmologist, Emile Javal in 1879 [5]. He discovered that eyes move in
a series of jumps (saccades) and pauses (fixations). His research was based only on
his direct observation of eyes, so it could not be fully reliable. First eye-tracker was
developed by Edmund Burke Huey in 1897. The way in which people read text was
the first area of interest. It turned out contrary to common point of view in those
times that people read more than one letter simultaneously. They read whole words
or even whole phrases. The nature of reading ability was examined and the results
were published in a comprehensive form in 1908 [6].
Other area of interest was how the brain processed images. It turned out that
placements and order of fixations were strictly dependent on the kind of picture that
was seen and on previous individual experience with that kind of pictures. The brain
was believed to be attracted by the most important elements of the picture, and, after
examining them, to focus on less important details. The acquired knowledge on the
way the brain was processing information was used mostly in psychological research
[7].
Another evolving field where eye trackers are used is research called usability
engineering the study of the way that users are interacting with products to improve
those products’ design. Among the most popular nowadays is the study of the
usability of WWW pages [3][8][9].
Although there has not been any research of using eye movements to perform
human identification, some authors noticed significant differences between people.
Josephson and Holmes [8] tested the scanpath theory introduced by Stark and Norton
[10] on three different WWW pages. They not only confirmed that individual learnt
scanpaths (series of fixations) and repeated it when exposed on the same stimulation
again, but they also noticed that each examined person learned a different scanpath.
Hornof and Halverson [11] tried to implement methods for automatic calibration of
eye tracker. What is worth mentioning, they noticed that calibration errors were
different for different persons and created the so-called ‘error signatures’ for each
person being examined.
There are a lot of studies comparing the eye-movements of different categories of
people, for instance males and females [9] or musicians and non-musicians [12].
4 Human identification with eye movements’ information
4.1 How to perform the experiment
As was described in the previous section, eye movements may give a lot of
information about an individual. The simplest way to obtain a probe seems to be just
recording eye movements of a person for a predefined time. The method is very
simple to conduct even without any cooperation from a person being identified. In
fact the person may not even be aware of being identified, which gives opportunity
for the so-called ‘covert’ identification. The main drawback of that method is the
obvious fact that eye movements are strongly correlated with the image they are
looking at. The movements would be quite different for a person looking at quickly
changing environment (for instance a sport event or an action movie) than for a
person looking simply at white solid wall. Of course one may say that human
identification should be independent of visual stimulation. Indeed, theoretically it
should be possible to extract identification patterns from every eye movement without
knowledge of the character of stimulation. However, that kind of extraction seems to
be very difficult and requires a lot of more comprehensive study and experiments.
On the basis of the described problems a simple upgrade of the system could add a
module which registers the image which is the ‘reason’ of eyes’ movements. In that
kind of model we have a dynamic system for which we are registering input and the
system’s answer for that input.
Such improvement gives a lot more data to analyze, yet it also has several serious
drawbacks. First of all, the hardware system is much more complicated. We need
additional camera recorder which registers the image the examined person is looking
at. Furthermore, we need to implement special algorithms to synchronize visual data
with eye movement signal. A lot more capacity is also needed for data storing.
We must additionally be aware that camera ‘sees’ the world differently than a
human eye, thus the image we register cannot be considered completely equivalent to
eyes’ input.
To be useful in biometric identification, a single experiment must be as short as
possible. With no influence on stimulation we cannot be sure that in the short time of
experiment we can register enough interesting information about a person being
identified. Therefore, the natural consequence of that fact is introducing our own
stimulation. That solution gives us opportunity for avoiding image registering,
because when stimulation is known, registration is not necessary.
Fig. 1. System generates visual stimulation and registers the oculomotor system reaction.
The most convenient source of visual stimulation is a computer display. However,
as in the previous methods, we should be aware of the fact that the monitor screen is
only a part of the image that eyes see, so not the whole input is measured.
Furthermore, the input may consist of non-visual signals. Sudden loud sounds may,
for instance, cause rapid eye movements [13]. Nevertheless, that kind of experiment
seems to be the simplest to realize.
4.2 Choosing stimulation
The problem which emerges here is ‘what should stimulation be?’
One may consider different types of stimulation for the experiment. The type of
stimulation implies what aspect of eye movements is measured.
The simplest one could be just a static image. As has been already stated, eyes are
moving constantly, even looking at the static image, to register every important
element of image with fovea region fixation. According to Stark and Norton [n03]
brain is creating a ‘scanpath’ of eye movements for each seen image.
The more sophisticated stimulation could be a dynamic one like a movie or
animation displayed on the screen. There may be different aspects of stimulation
considered: color, intensity, speed, etc.
The problem with using eye movements in biometrics is that the same experiment
will be performed on the same person a number of times. Firstly, there should be
several experiments performed to enroll user characteristic. Then, the experiment is
performed each time an individual wants to identify himself. It may be supposed that
if the same stimulation is used each time, a person would be bored with it and eye
movements would not be very interesting. The problem may be called a ‘learning
effect’ as the brain learns the stimulation and acts differently after several repetitions
of the same image.
The solution could be presenting different stimulation each time. The different
types of stimulation should be as similar as possible to enable extraction of the same
eye movements parameters for future analyses. On the other hand, they should be so
different that a learning effect would be eliminated. The task is therefore not an easy
one.
To be sure that an examined person cooperates with the system (e.g. they move
their eyes) a special kind of stimulation called ‘visual task’ may be proposed. The
visual task may be for instance finding a matching picture [11] or finding missing
elements on a known picture [14],
A special kind of visual task could be a text reading task. In such an experiment a
person just reads the text appearing on the screen. There are a lot of studies
concerning eye movement tracking while reading a text and they give very interesting
results [15][16]. Especially there are some theoretical models of human eyes
movements like SWIFT [16]. After years of usage the human brain is very well
prepared to control eye movements while reading a text and each human being has
slightly different customs and habits based on different ‘reading experience’.
Therefore, it may be assumed that by analyzing the way a person reads a specially
prepared text a lot of interesting information may be extracted. And indeed there are a
lot of researches concerning that subject [15][16][7].
Yet, when applying that to identification system, the problem of the learning effect
appears once again. We may for instance notice that a person has a lot of problems
with reading the word ‘oculomotor’. However, presenting that word during each
experiment causes that person’s brain gets familiar with the word and that effect
disappears.
Thus, the learning effect makes it impossible to repeat the same experiment and to
get the same results. In other words, the experiment is not stationary for a number of
trials. Each experiment performed on the same person is different, because previous
experiments have changed the brain parameters. Of course that effect is clearly visible
onl y when a lot of experiments are conducted on the same person .
It is a serious problem because, contrary to other eye movements experiments, the
main attribute of identification system should be its repeatability for the same person.
The experiment must be performed a lot of times giving the same results. A learning
effect makes it very difficult.
A solution of that problem may be a stimulation which imposes on a person a spot
the eyes should look at. The simplest form of that stimulation may be a ‘wandering
point’ stimulation. It that kind of stimulation the screen is blank with only one point
‘wandering’ through it. The task of examined person is to follow the point with the
eyes.
It is easier to analyze results of that kind of stimulation. That time we are not
interested in examining where the person is looking but in examining how they look
at the point. We may suppose that all results will be more or less similar to one
another and our task is to extract the differences among people.
The main drawback of the method, however, is that it completely ignores the will
of the person. The person cannot decide where to look at the moment and therefore
we are loosing all information from brain’s ‘decision centre’. We may say that that
kind of stimulation examines more the oculomotor system than the brain.
However, using it we can overcome the learning effect. Obviously it still exists, but
in that experiment it may become our ally. A person who looks at the same
stimulation a lot of times gets used to it and the results of the following experiments
are converging to the same point.
Having that in mind we can suppose that, after a specific number of experiments,
next probes will be very similar and therefore easier to identify. It is exactly the same
effect as for the written signature:
A person is asked to write a word on the paper. It may be their surname for
instance. The word on the paper looks typical for their handwriting style and it is
possible for a specialist to identify them. Yet, when they are asked to write the same
word over and over again, they get used to it and the brain produces the kind of
automatic schema for performing that task. At this moment the handwritten word
looks very similar every time and that is what we call a signature. Contrary to
handwriting, the signature may be recognized even by an unqualified person for
instance a shop assistant.
We would like to use the same effect with the eye movements. First, we show a
person the same stimulation several (as many as possible) times. After that process,
the person’s brain produces an automatic schema and results of the following
experiments will start to converge. It, of course, makes the process of recognition
(identification) easier remember a handwriting specialist versus a shop assistant.
5 Experiment
The stimulation which has been eventually chosen was a ‘jumping point’ kind of
stimulation. There are nine different point placements defined on the screen, one in
the middle and eight on the edges, creating 3 x 3 matrix. The point flashes in one
placement in a given moment. The stimulation begins and ends with a point in the
middle of the screen. During the stimulation, point’s placement changes in specified
intervals.
That kind of stimulation has the advantage over others (especially ‘wandering
point’) that it is very easy to perform even without a display. In fact, there are only
nine sources of light needed (for instance just simple diodes).
The main problem in developing stimulation is to make it short and informative.
Those properties are as if on two opposite poles, so a ‘golden mean’ must be found.
We assumed that gathering one probe could not last longer than 10 seconds. To be
informative it should consist of as many point position changes as possible. However,
moving a point too quickly makes it impossible for eyes to follow it. Our
experiments confirmed that the reaction time for change of stimulation is about 100-
200 ms [14]. After that time eyes start a saccade which moves fovea to the new gaze
point. The saccade is very fast and lasts not longer than 10-20 ms. After a saccade, the
brain analyses a new position of the eyes and, if necessary, tries to correct it. So very
often about 50 ms after the first saccade the next saccade happens. We can call it a
‘calibration’ saccade.
Fig. 2. Typical eye movement reaction for point jump in one axis. Reaction time is understood
as the period of time between stimulation’s change and eye reaction. Stabilization time is the
time until fixation on a new gaze-point. Two calibration saccades may be observed.
5.1 Feature extraction
After collecting data the main problem is how to extract information which is useful
for human identification.
The dataset consists of probes. Each probe is the result of recording of one
person’s eye movements during about 8 seconds-lasting stimulation. As the
experiments were made with frequency 250Hz the probe consists of 2048 single
measurements. Each measurement consists of six integer values which are giving the
position of a stimulation point on the screen (sx, sy), the position of the point the left
eye is looking at (lx, ly) and the position of the point the right eye is looking at (rx,
ry).
So in each experiment we are collecting a probe of 2048 x 6 = 12288 integer
values.
The next step is to convert those values into a set of features. Each feature gives
some information about a person who made that experiment. That information may be
understandable for instance “he is a male” or “his reaction time is less than 200 ms”,
but the meaning of the feature may be hidden also, giving only the value.
Fig. 3. Registered eye movements of left eye in horizontal axis as the answer to stimulation.
Reaction times and drifts may be observed. One experiment gives four such waveforms.
The main problem is how to extract a set of features that have values for different
probes of the same person as similar as possible and that have values for different
person’s probes as different as possible. The perfect solution would be finding
features which have exactly the same values for the same person in every experiment.
As it was mentioned earlier, identification based on eye movement analysis is a
brand new technique. The main disadvantage of that technique is that one cannot use
recently published algorithms and just try to improve it with one’s own ideas.
Therefore, we could only try to use methods which have been successfully used with
similar problems.
There are many different possibilities, for instance Karhunen-Loeve transform
(popular PCA) [17], Fourier transform, cepstrum [21] or wavelets. Each of those
techniques has been successfully used in different biometric applications like face,
finger, voice or iris recognition. What may also be considered are features specific for
the nature of the eye movement signal: average reaction time, average stabilization
time, saccade velocity and acceleration etc..
It seems that each method may work as good as others. Therefore, a lot of work has
to be done to compare those techniques. Cepstrum was used in the present work
because of its success in voice recognition. The cepstrum is counted as the inverse
Fourier transform of the logarithm of the power spectrum of a signal [21].
5.2 Classification
Having extracted features that are hopefully relevant to identification, the next step is
to prepare a classification model. When we obtain the next unclassified probe, it
would be possible, using that model, to estimate probability that the specific probe
belongs to a specified person.
There are a lot of classification algorithms which can be used here. Their main
property is creating a model based on information obtained from training data a set
of classified probes. Algorithms try to generalize rules that were found in the training
set and to prepare a function which could be used to classify future probes.
Of course that generalization depends not only on the algorithm itself, but mostly
on characteristic of a training data. Only if the training set is representative of the
whole population and there are some (even hidden) rules in its features that could be
helpful, classifying algorithm may work well. Therefore, proper feature extraction is
the crucial element of that work.
There were four different classifying algorithms used in the present paper. Namely,
k-Nearest Neighbor (for k=3 and k=7), Naïve Bayes [20], C4.5 Decision Tree [18]
and Support Vector Machines [19]. Due to the lack of space for description of the
algorithms, the reader directed to the referred materials.
6 Results
The experiment was performed on nine participants. Each participant was enrolled
over 30 times and last 30 trials were used for classification, giving 270 probes for a
training set.
First 15 cepstral coefficients were extracted from each of four waveforms. That
gave 60 dimensional vectors for each probe. Then, nine training sets were generated
each for classifying one participant. One classifying model was generated from each
set using different classification algorithms.
Table 1. Results of data validation for different classification algorithms. Averages of false
acceptance and false rejection rates (FAR and FRR) are counted from nine generated models
Algorithm Average FAR Average FRR
Naïve Bayes 17.49 % 12.59 %
C45 Decision Tree 3.33 % 35.56 %
SVM polynomial 1.60 % 28.89 %
KNN k=3 1.48 % 22.59 %
KNN k=7 1.36 % 28.89 %
Each classification model was cross-validated using 10 fold stratified cross
validation [20]. K-Nearest Neighbor algorithm with k=3 performed best with the
average false acceptance rate 1.48 % with the best result 0.4 % and the worst result
2.5 % and false rejection rate in range of 6.7 % to 43.3 % with average 22.59 %.
7 Conclusion
The idea of personal identification using eye movement characteristic presented in the
paper seems to be valuable addition to other well known biometric techniques. What
makes it interesting is the easiness of combining it with, for instance, face or iris
recognition. As all of those techniques need digital cameras to collect data, the system
that uses the same recording devices to gather information about human face shape,
eye iris pattern and last but not least eye movements characteristic may be
developed. Of course there is a lot of work to be done to improve and test our
methodology, but first experiments show the great potential of eye movements
identification. That potential was acknowledged during 6
th World Conference
BIOMETRICS’2003 in London where a poster ‘Eye movement tracking for human
identification’ [22] was awarded the title of ‘Best Poster on Technological
Advancement’ and that encourages future effort.
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... Using eye movements to recognize persons was pioneered by Kasprowski and Ober [3] and has gained attention since then [4]. One advantage of eye movement biometrics is that eye movements cannot be forged. ...
... Kasprowski and Ober [3] first explored eye movements in biometrics. They used "jumping" points as stimuli and a Cepstrum transform for the features. ...
... In summary, the performance of eye movement biometrics has improved significantly since it was first proposed in 2004 [3]. Refer to [4,30] for detailed reviews. ...
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Eye movement has become a new behavioral feature for biometric authentication. In the eye movement-based authentication methods that use temporal features and artificial design features, the required duration of eye movement recordings are too long to be applied. Therefore, this study aims at using eye movement recordings with shorter duration to realize authentication. And we give out a reasonable eye movement recording duration that should be less than 12 s, referring to the changing pattern of the deviation degree between the gaze point and the stimulus point on the screen. In this study, the temporal motion features of the gaze points and the spatial distribution features of the saccade are using to represent the personal identity. Two datasets are constructed for the experiments, including 5 s and 12 s of eye movement recordings. On the datasets constructed in this paper, the open-set authentication results show that the Equal Error Rate of our proposed methods can reach 10.62% when recording duration is 12 s and 12.48% when recording duration is 5 s. The closed-set authentication results show that the Equal Error Rate of our proposed methods can reach 5.25% when recording duration is 12 s and 7.82% when recording duration is 5 s. It demonstrates that the proposed method provides a reference for the eye movements data-based identity authentication.
... Since the use of eye movement in biometrics was first introduced in 2004 [ Kasprowski and Ober 2004], various research has been carried out to improve its performance [Lohr et al. 2022]. Such research used multiple types of eye movement tasks, including moving dot [Kasprowski and Ober 2004], reading text [Lohr et al. 2020], video viewing, and image viewing [Jia et al. 2018]. ...
... Since the use of eye movement in biometrics was first introduced in 2004 [ Kasprowski and Ober 2004], various research has been carried out to improve its performance [Lohr et al. 2022]. Such research used multiple types of eye movement tasks, including moving dot [Kasprowski and Ober 2004], reading text [Lohr et al. 2020], video viewing, and image viewing [Jia et al. 2018]. Several methodologies to improve eye movement-based authentication were explored, including radial basis function network [George and Routray 2016] and deep learning [Jia et al. 2018]. ...
Poster
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The viability and need for eye movement-based authentication has been well established in light of the recent adoption of Virtual Reality headsets and Augmented Reality glasses. Previous research has demonstrated the practicality of eye movement-based authenti-cation, but there still remains space for improvement in achieving higher identification accuracy. In this study, we focus on incorporating linguistic features in eye movement based authentication, and we compare our approach to authentication based purely on common first-order metrics across 9 machine learning models. Using GazeBase, a large eye movement dataset with 322 participants, and the CELEX lexical database, we show that AdaBoost classifier is the best performing model with an average F1 score of 74.6%. More importantly , we show that the use of linguistic features increased the accuracy of most classification models. Our results provide insights on the use of machine learning models, and motivate more work on incorporating text analysis in eye movement based authentication.
... With the help of this oculomotor system, the image seen by the eye is projected onto the retina of the eye. The retina has light-sensitive cells which convert the light into signals which are then given to the brain [11]. The light-sensitive cells are not evenly distributed with maximum density at the centre. ...
... During this time, the object on the fovea is analyzed by the brain. The standard duration for fixation can be between 200 ms to 300 ms [11]. Then there is a rapid eye movement during which the eye moves from one fixation to another fixation. ...
Thesis
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This thesis works with the eye-tracking data in the project 'Schau mir in die Augen'. Abstract The study includes working with eye movements for user identification. The main objective of the thesis is to improve the accuracy by modifying a pre-existing pipeline to recognize and take advantage of outliers. For this goal, the reasons for outliers and their particularities are investigated. Various methods are used and compared for detecting the outliers. It is found that removing the outliers from the pipeline decreases the accuracy. Instead, it is studied to include them in the pipeline. For this, a new classifier is added into the pipeline along with fixations and saccades, which deal with outliers. All the experiments are performed on the Bio-Tex dataset and lead to an improvement of accuracy. The final pipeline is tried on the Bio-Ran dataset, but there are no conclusive results, and more experiments need to be performed. Two classifiers are used in the thesis, majorly is Random Forest, and the other is Radial Basis Function Network. The Bio-Tex shows an improvement in accuracy by considering the outliers for the two classifiers. For Bio-Ran, there are no conclusive results obtained. Outliers play a vital role in eye movements and show the potential to improve the accuracy in the user identification field with more research.
... Several research projects have proposed these kinds of authentication schemes. Eye movement biometrics for human identification were initially investigated by Kasprowski and Ober [14]. They performed a personalized identification based on characteristic eye movement, specifically using eye tracking equipment. ...
Article
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This paper addresses the implementation of an eye detection mobile application for unlocking mobile devices using its built camera as an eye tracker. The implementation allows a real-time face and eye detection of a mobile phone user. The idea behind our development was the creation of a preventive model for the benefit and safety of mobile users, including disabled users (e.g., users with arthritis). The product is a visual application that detects the pupils and can simulate the mouse cursor on the screen to unlock the device. Although some screen lock methods have been already developed, our project aims to complement the security in mobile devices. Moreover, with a new mechanism to unlock mobile devices it would offer a more robust alternative in terms of safety of such devices. Evaluation results suggest our implementation is a promising alternative for security in these devices.
... Today, a vast number of different eye tracking devices are available and have been used by researchers to produce many high-quality datasets. These datasets are analyzed in different contexts; we only mention a few examples: disorder detection [3][4][5], gaming [1,23,25], gender prediction [27,35,44], and user identification [14,20,33,40] Let us consider the state-of-the-art approaches to user identification via eye movement data. In the 2015 BioEye competition [33] different systems for user identification were competing. ...
Preprint
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Several studies have reported that biometric identification based on eye movement characteristics can be used for authentication. This paper provides an extensive study of user identification via eye movements across multiple datasets based on an improved version of method originally proposed by George and Routray. We analyzed our method with respect to several factors that affect the identification accuracy, such as the type of stimulus, the IVT parameters (used for segmenting the trajectories into fixation and saccades), adding new features such as higher-order derivatives of eye movements, the inclusion of blink information, template aging, age and gender.We find that three methods namely selecting optimal IVT parameters, adding higher-order derivatives features and including an additional blink classifier have a positive impact on the identification accuracy. The improvements range from a few percentage points, up to an impressive 9 % increase on one of the datasets.
Article
The recent COVID-19 outbreak has highlighted the importance of contactless authentication methods, such as those based on eye or gaze features. These techniques have the advantage that they can also be used by people wearing mouth and nose masks, which would make traditional face recognition approaches difficult to apply. Moreover, they can be used in addition to traditional authentication solutions, such as those based on passwords or PINs. In this work, we propose a study on gaze-based soft biometrics exploiting simple animations as visual stimuli. Specifically, we consider four animations in which small squares move according to different patterns and trajectories. No preliminary calibration of the eye tracking device is required. The collected data were analyzed using machine learning algorithms for both identification and verification tasks. The obtained results are particularly interesting in the verification case, that is the natural application of a soft biometric system, with accuracy scores always higher than 80% and Equal Error Rate (EER) values often lower than 10%.
Article
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An experiment is reported that investigated the application of eye movement analysis in the evaluation of webpage usability. Participants completed two tasks on each of four website homepages. Eye movements and performance data (Response Scores and Task Completion Times) were recorded. Analyses of performance data provided reliable evidence for a variety of Page and Task effects, including a Page by Task interaction. Four eye movement measures (Average Fixation Duration, Number of Fixations, Spatial Density of Fixations, and Total Fixation Duration) were also analysed statistically, and were found to be sensitive to similar patterns of difference between Pages and Tasks that were evident in the performance data, including the Page by Task interaction. However, this interaction failed to emerge as a significant effect (although the main effects of Page and Task did). We discuss possible reasons for the nonsignificance of the interaction, and propose that for eye movement analysis to be maximally useful in interface-evaluation studies, the method needs to be refined to accommodate the temporal and dynamic aspects of interface use, such as the stage of task processing that is being engaged in.
Article
Full-text available
An experiment is reported that investigated the application of eye movement analysis in the evaluation of webpage usability. Participants completed two tasks on each of four website homepages. Eye movements and performance data (Response Scores and Task Completion Times) were recorded. Analyses of performance data provided reliable evidence for a variety of Page and Task effects, including a Page by Task interaction. Four eye movement measures (Average Fixation Duration, Number of Fixations, Spatial Density of Fixations, and Total Fixation Duration) were also analysed statistically, and were found to be sensitive to similar patterns of difference between Pages and Tasks that were evident in the performance data, including the Page by Task interaction. However, this interaction failed to emerge as a significant effect (although the main effects of Page and Task did). We discuss possible reasons for the non-significance of the interaction, and propose that for eye movement analysis to be maximally useful in interface-evaluation studies, the method needs to be refined to accommodate the temporal and dynamic aspects of interface use, such as the stage of task processing that is being engaged in.
Article
Full-text available
Perceiver eye movements were recorded during audiovisual presentations of extended monologues. Monologues were presented at different image sizes and with different levels of acoustic masking noise. Two clear targets of gaze fixation were identified, the eyes and the mouth. Regardless of image size, perceivers of both Japanese and English gazed more at the mouth as masking noise levels increased. However, even at thehighest noise levels and largest image sizes, subjects gazed at the mouth only about half the time. For the eye target, perceivers typically gazed at one eye more than the other, and the tendency became stronger at higher noise levels. English perceivers displayed more variety of gaze-sequence patterns (e.g., left eye to mouth to left eye to right eye) and persisted in using them at higher noise levels than did Japanese perceivers. No segment-level correlations were found between perceiver eye motions and phoneme identity of the stimuli.
Article
The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Conference Paper
The somewhat controversial and often-discussed theory of visual perception, that of scanpaths, was tested using Web pages as visual stimuli. In 1971, Noton and Stark defined "scanpaths" as repetitive sequences of fixations and saccades that occur upon re-exposure to a visual stimulus, facilitating recognition of that stimulus. Since Internet users are repeatedly exposed to certain visual displays of information, the Web is an ideal stimulus to test this theory. Eye-movement measures were recorded while subjects repeatedly viewed three different kinds of Internet pages -- a portal page, an advertising page and a news story page -- over the course of a week. Scanpaths were compared by using the string-edit methodology that measures resemblance between sequences. Findings show that on the World Wide Web, with somewhat complex visual digital images, some viewers' eye movements may follow a habitually preferred path -- a scanpath -- across the visual display. In addition, strong similarity among eye-path sequences of different viewers may indicate that other forces such as features of the Web site or memory are important.
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It has long been debated whether eye movements play a functional role in visual mental imagery. A recent paper by Laeng and Teodorescu presents new evidence that eye movements are stored as a spatial index that is used to arrange the component parts correctly when mental images are generated.
Article
The study aims to test whether the eye movements (saccades) of professional musicians differ from a corresponding non- musician control group. It is assumed that the early commencement of instrumental practice, which is characterized by demanding "visual input" for the perceptual system (such as the reading of musical notation), can modify the way visual information is processed in professional adult musicians. It can be assumed that the movement parameters of the oculomotoric system reveal a kind of "fingerprint" of a person's way of processing information. However, up until now, no data has been available that could illuminate special features of eye movements in musicians compared to non-musicians. An electrooculogram (EOG) was used to obtain eye movement data (horizontal movements only) from 8 professional musicians (pianists) while performing an oculomotoric tracking task on a screen (hereafter called the "jumping point"). The jumping point's velocity increased over 90 seconds from 0.2 to 1.5 Hz, following a rectangular waveform of movement. An extensive sample of psychology students (n = 254) served as the control group. The data revealed clear differences in eye movement parameters between musicians and non-musicians: The 'musicians' glance' of our sample was characterized by a considerably reduced frequency of omissions, shorter reaction time in reactive saccades, a higher proportion of anticipatory saccades, higher saccade velocity and shorter anticipatory latencies. Our carefully selected group of musicians seem to be characterized by extremely efficient strategies for the processing of visual information in an oculomotoric task. All findings indicate a strong tendency for optimized task adaptation in professional musicians for a task which is different from the domain of music. Results are discussed within the framework of general mental ability for the efficient optimization of task adaptation.
Article
The OBER 2 is an infrared light eye movement measuring system and it works with IBM PC compatible computers. As one of the safest systems for measuring of eye movement it uses a very short period of infrared light flashing time (80 microsecond for each measure point). System has an advanced analog-digital controller, which includes background suppression and prediction mechanisms guaranteeing elimination of slow changes and fluctuations of external illumination frequency up to 100 Hz, with effectiveness better than 40 dB. Setting from PC the active measure axis, sampling rate (25 - 4000 Hz) and making start and stop the measure, make it possible to control the outside environment in real-time. By proper controlling of gain it is possible to get high time and position resolution of 0.5 minute of arc even for big amplitude of eye movement (plus or minus 20 degree of visual angle). The whole communication system can also be driven directly by eye movement in real time. The possibility of automatic selection of the most essential elements of eye movement, individual for each person and those that take place for each person in determined situations of life independently from personal features, is a key to practical application. Hence one of conducted research topic is a personal identification based on personal features. Another task is a research project of falling asleep detection, which can be applied to warn the drivers before falling asleep while driving. This measuring system with a proper expert system can also be used to detect a dyslexia and other disabilities of the optic system.