Eyetrace2014

Eyetracking Data Analysis Tool

Katrin Sippel

, Thomas K

ubler

, Wolfgang Fuhl

, Guilherme Schievelbein

, Raphael Rosenberg

and Wolfgang Rosenstiel

Wilhelm Schickard Institute for Computer Science, Computer Engineering Department,

University of T

ubingen, T

ubingen, Germany

Department of Art History, University of Vienna, Vienna, Austria

Keywords:

Eye-tracking, Analysis Software, Fixation Identiﬁcation, Clustering, Areas of Interest.

Abstract:

Over the last years eye tracking became more and more popular. A variety of new eye-tracker models and

algorithms for eye tracking data processing emerged. On the one hand this multitude of hard- and software

brought many advantages, on the other hand the diversity of devices and measures impedes the comparability

and repeatability of eye-tracking studies. While supply of eye tracking software is high, the functioning of the

algorithms, e.g. how ﬁxations are identiﬁed, is often intransparent and unﬂexible. The Eyetrace software bun-

dle approaches these problems by providing a variety of different evaluation methods compatible with many

eye-tracker models.

Eyetrace2014 combines state-of-the-art algorithms with established approaches and provides a continuous

visualization of the analysis process. All calculations provide user adaptable parameters and are well docu-

mented and referenced in order to make the whole analysis transparent.

Our software is available free of charge well suited for exploratory data analysis and education

(http://www.ti.uni-tuebingen.de/Eyetrace.1751.0.html).

1 INTRODUCTION

During the last decades, eye-tracking became resi-

dent in many ﬁelds of application. Besides its tra-

ditional use in psychology and market investigation,

eye-tracking also found its way into medicine and nat-

ural sciences. Going hand in hand with the increase in

the number of eye-tracking devices and vendors, the

variety of software for the evaluation of the produced

data increased steadily (SMI begaze, Tobii Analytics,

D-Lab, NYAN, Eyeworks, ASL Results Plus, Gaze-

point Analysis, ...). Major brands offer their indi-

vidual analysis software with ready-to-run algorithms

and preset parameters for their eye-tracker model and

typical application. However, all of them share a com-

mon feature base (such as visualizing gaze traces, at-

tention maps, gaze clusters and calculating area of in-

terest statistics) and distinguish in minor features.

Besides the ﬁnancial effort and licensing restric-

tions, these applications usually can not be ex-

tended by custom algorithms and specialized evalu-

ation methods. Not few studies reach the point where

the manufacturer software is insufﬁcient or very ex-

pensive, extension of the software is not possible and

all data has to be exported and loaded into other pro-

grams e.g. Matlab for further processing. Further-

more individual calculations are often non-opaque or

not documented in all necessary detail in order to al-

low comparison to studies conducted with different

eye-tracker models or even different recording soft-

ware versions.

Eyetrace supports a range of common eye-

trackers and offers a variety of state-of-the-art algo-

rithms for eye-tracking data analysis. The aim is not

only to provide a standardized work ﬂow, but also to

highlight the variability of different eye-tracker mod-

els as well as different algorithms (such as ﬁxation

identiﬁcation ﬁlters). Our approach is driven by con-

tinuous data visualization so that the result of each

analysis step can be visually inspected. Different vi-

sualization techniques are available and can be active

at the same time, i.e. a scanpath can be drawn over

an attention map with areas of interest highlighted.

All visualizations are customizable in order to visual-

ize grouping effects, being distinguishable on differ-

ent backgrounds and for color-blind persons.

212

Sippel K., Kübler T., Fuhl W., Schievelbein G., Rosenberg R. and Rosenstiel W..

Eyetrace2014 - Eyetracking Data Analysis Tool.

DOI: 10.5220/0005352902120219

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2015), pages 212-219

ISBN: 978-989-758-068-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

We realized that no analysis software can provide

all the tools required for every possible study. There-

fore the software is on the one hand extensible and

offers on the other hand the possibility for data export

of all calculated values.

Eyetrace has its root in a collaboration between

the department of art history (Brinkmann et al., 2014;

Klein et al., 2014; Rosenberg, 2014) at the Univer-

sity of Vienna and the computer science department

at the University of T

ubingen, contributing new scan-

path evaluation tools and algorithms. It consists of

the core analysis component and a pre-processing step

that is responsible for compatibility with many differ-

ent eye-tracker models. The software bundle, includ-

ing Eyetrace2014 and EyetraceButler was written in

C++, based on the experience of the previous version

(EyeTrace 3.10.4, developed by M. Hirschb

uhl) as

well as other eye-tracking analysis tools (Tafaj et al.,

2011).

We are eager to implement state-of-the-art algo-

rithms, such as ﬁxation ﬁlters, clustering algorithms

and data-driven area of interest annotation and we

share the need to understand how these methods

work. Therefore implemented methods as well as

their parameters are transparent and documented in

detail with original work referenced. We provide a

standard set of parameters for the algorithms, but each

of them can easily be changed from within the GUI.

Eyetrace is available free of charge for universi-

ties and educational institutions. It is on the one hand

usable for complex scientiﬁc evaluations and on the

other hand also a intuitive tool to get familiar with

evaluation algorithms. Using Eyetrace for the practi-

cal part of an eye-tracking lecture it can help students

to reach a deeper understanding of eye-tracking data

analysis.

2 DATA PREPARATION

In order to make the use of different eye-tracker mod-

els convenient, recordings have to be preprocessed

and converted to a common eye-tracker independent

format. This step can also be used in order to splice

a single recording into subsets (e.g. by task or stimu-

lus) and for quality checking. This step is performed

by EyetraceButler.

EyetraceButler provides a separate plug-in for all

supported eye-trackers and converts the individual

eye-tracking recordings into a format that holds infor-

mation common to almost all eye-tracking formats:

For both eyes it contains the x and y coordinate, the

width and height of the pupil as well as a validity bit,

together with a joint time stamp. For monocular eye-

trackers or eye-trackers that do not include pupil data

the corresponding values are set to zero. A quality re-

port is produced that contains information about the

overall tracking quality as well as individual tracking

losses (Figure 1).

Figure 1: Quality analysis for two recordings with a binoc-

ular eye-tracker. The color codes measurement errors (red),

successful tracking of both eyes (green) and of only one eye

(yellow) over time. It is easy to visually assess the quality of

a recording, even if the beginning and end of the measure-

ment are of bad quality (top) or tracking is lost and regained

during the experiment (bottom).

2.1 Supplementary Data

In addition to the eye-tracking data, arbitrary supple-

mentary information about the subject or relevant ex-

perimental conditions can be added, e.g. gender, age,

dominant eye, or patient status. This information is

made available to Eyetrace2014 along with informa-

tion about the stimulus viewed. Using the provided

information the program is able to sort and group all

loaded examinations according to these values.

2.2 Supported Eye-trackers

The EyetraceButler utilizes slim plug-ins in order to

implement new eye-tracker proﬁles. As of now plug-

ins for ﬁve different eye-trackers are available, among

them models of SMI, Ergoneers and TheEyeTribe as

well as a calibration free tracker recently developed

by the Fraunhofer Institute in Illmenau.

3 DATA ANALYSIS

3.1 Loading, Grouping and Filtering

Data

Data ﬁles prepared by the Butler can be batch loaded

into Eyetrace together with their accompanying infor-

mation such as the stimulus image or subject informa-

tion. Visualization and analysis techniques can han-

dle subjects grouping by any of the arbitrary subject

information ﬁelds. For example attention maps can

be calculated separately for each subject, cumulative

for all subjects or by subject groups. This allows to

compare subjects with healthy vision to a low vision

patient group or to compare the viewing behavior of

different age groups. Adaptive ﬁlters are provided to

select the desired grouping and individual recordings

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can be included or excluded from the visualization

and analysis process.

3.2 Fixation and Saccade Identiﬁcation

One of the earliest and most frequent analysis steps

is the identiﬁcation of ﬁxations and saccades. Their

exact identiﬁcation is essential for the calculation of

many scan pattern characteristics, such as the average

ﬁxation time or saccade length.

Eye-tracking manufacturers often offer the possi-

bility to identify ﬁxations and saccades automatically.

However, this ﬁlter step is not as trivial as the auto-

mated annotation may suggest. In fact, different algo-

rithms yield quite different results. By offering a va-

riety of calculation methods and making their param-

eters available for editing, we want to bring to mind

the importance of the right choice of parameters. Es-

pecially when it comes to identifying the exact ﬁrst

and last point that still belong to a ﬁxation and the

merging of subsequent ﬁxations that come to fall to

the same location, relevant differences between algo-

rithms and a high sensitivity to parameter changes can

be observed.

As of now, algorithms of the following categories

are implemented: spatial threshold-based approaches

with minimum ﬁxation duration and maximum

spread, velocity-threshold and a Gaussian mixture

model (Tafaj et al., 2012) that adaptively learns from

the data and does not require any thresholds to be set.

Standard Algorithm

The standard algorithm for separating ﬁxations and

saccades is based on three adjustable values: The

minimum duration of the ﬁxations, the maximum

radius of the ﬁxations and the maximum number

of points that are allowed to be outside this radius

(helpful with noisy data). A time window of the

minimum ﬁxation duration is shifted over the mea-

surement points until the conditions of maximum

radius and maximum outliers are fulﬁlled. In the

following step the beginning ﬁxation is extended if

possible until the number of allowed outliers has

been reached. A complete ﬁxation has been identiﬁed

and the procedure starts anew. Every measurement

point that was not assigned to a ﬁxation is assigned

to the saccade between its predecessor and successor

ﬁxation.

Velocity Based Algorithm

Since saccades show high eye movement speed while

ﬁxations and smooth pursuit movements are much

slower, putting a threshold on the eye movement

speed is a straight forward way of ﬁxation ﬁltering.

Eyetrace2014 currently implements three different

variants of velocity based ﬁxation identiﬁcation.

Each of the methods can ﬁlter short ﬁxations via a

minimum duration in a post-processing step.

Velocity Threshold by Pixel Speed [px/s]. A simple

threshold over the speed between subsequent mea-

surements. If the speed is exceeded, the measurement

belongs to a saccade, otherwise to a ﬁxation. While

a pixel per second threshold is easy to interpret

for the computer, it is often not meaningful to the

experimenter and therefore hard to choose.

Velocity Threshold by Percentile. Based on the

assumption that the velocity is bigger within saccades

than within ﬁxations, velocities are sorted by magni-

tude and a threshold is chosen by a percentile of the

data selected by the user (usually 80-90%).

An example of sorted distances between measure-

ments:

1 3 7 11 12 13 18 21 21 22

Green distances are supposed to belong to ﬁxations

for a 60% percentile (6 out of 10 distances) and the

value 18 would be chosen as velocity threshold.

Velocity Threshold by Angular Velocity [/s]. This

is the representation most common in the literature

since it is independent of pixel count and individual

viewing behavior. However, it also requires most

knowledge about the data recording process in order

to be able to convert the pixel distances into angular

distances (namely the distance between viewer and

screen, screen width and resolution). Suggested

values for individual tasks can be found in the

literature (Blignaut, 2009; Salvucci and Goldberg,

2000)

Gaussian Mixture Model

A Gaussian mixture model as introduced in (Tafaj

et al., 2012) is also available. This method is based

on the assumption that distances between subsequent

measurement points within a ﬁxation form a Gaussian

distribution. Furthermore distances between mea-

surement points that belong to a saccade also form

a Gaussian distribution, but with different mean and

standard deviation. A maximum likelihood estima-

tion of the parameters of the Mixture of Gaussians

is performed. Afterward for each measurement point

the probability that it belongs to a ﬁxation or to a sac-

cade can be calculated and ﬁxation/saccade labels are

assigned based on these probabilities (Figure 2). The

major advantage of this approach is that all parame-

ters can be derived from the data. One could evaluate

data recorded during an unknown experiment with-

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out the need to specify any thresholds or experimental

conditions. The method has been evaluated in several

studies (Kasneci et al., 2014a; Kasneci et al., 2015).

Probability density

Distance between measurements

Fixations

Saccades

Figure 2: Fit of two Gaussian distributions to the large dis-

tances between subsequent measurements within saccades

and the short distances between ﬁxations. Two sample

points are shown, one with higher probability to belong to

a ﬁxation (left) and one with a higher probability for a sac-

cade (right).

3.3 Fixation Clustering

After identiﬁcation of ﬁxations and saccades the ﬁx-

ations can also be clustered. Areas with a high den-

sity of ﬁxations are likely to contain semantically rel-

evant objects. Clustering ﬁxations either by neigh-

borhood thresholds or mean-shift clustering (as pro-

posed by (Santella and DeCarlo, 2004)) results in

data-driven, automatically assigned areas of interest.

This step reduces time consuming manual annotation

and enables data analysis without prior knowledge of

the analyst inﬂuencing the results (Figure 4).

Clusters of saccades correspond to frequent paths

taken by the eyes. Fixation and saccade clusters can

be calculated on the scan patterns of one subject or

cumulative on a group of subjects.

Standard Clustering Algorithm

This greedy algorithm requires the deﬁnition of a

minimum number of ﬁxations that will be considered

a cluster and the maximum radius of a cluster. Fix-

ations are sorted in descending order of the number

of included gaze points. Starting with the longest

ﬁxation, the algorithm iterates over all ﬁxations,

checking whether they fulﬁll the conditions of

building a cluster with the biggest one. If the number

of found ﬁxations is sufﬁcient, all found ﬁxations

are assigned to the same cluster and excluded from

further clustering. If not, the ﬁrst ﬁxation cannot be

assigned to any cluster and the algorithm starts again

from the second longest ﬁxation.

Mean-shift Clustering

The mean-shift clustering method assumes that mea-

surements are sampled from Gaussian distributions

around the cluster centers. The algorithm converges

towards local point density maxima. The iterative

procedure is shown in Figure 3. One of the main

advantages is that it does not require the expected

number of clusters in advance but determines an

optimal clustering based on the data.

Figure 3: Simpliﬁed visualization of the mean-shift algo-

rithm for the ﬁrst two iterations at one starting point. In each

iteration the mean (green square) of all data points (blue cir-

cles) within a certain window around a point (big red circle)

is calculated. In the next iteration the procedure is repeated

with the window shifted towards the previous mean. This is

done until the mean convergence.

Cumulative Clustering

The clustering algorithms mentioned above can also

be used on the cumulative data of more than one sub-

ject or more than one experiment condition. This way

cumulative population clusters can be formed. They

are more robust to noise and individual viewing be-

havior differences. The parameters of the algorithms

are adapted for cumulative usage (e.g. the number

of minimum ﬁxations for the standard algorithm de-

pends on the number of data sets used for cumulative

analysis), but the way the methods work remain the

same.

3.4 Areas of Interest (AOIs)

For the evaluation of speciﬁc regions, Eyetrace2014

provides the possibility to annotate AOIs manually or

by automatic conversion of ﬁxation clusters. While

the automated way is comfortable, the generated

AOIs are not required to intuitively make sense. An-

notating semantically meaningful areas is still done

best by a human. Therefore we provide a graphi-

cal editor where polygonal AOIs can be deﬁned and

edited with few mouse clicks. Figure 4 shows an ex-

ample of manually and automatically deﬁned AOIs

and also visualizes that automatically generated AOIs

tend to - but do not necessarily - correspond to inter-

esting regions of the image. All generated AOIs can

be saved to disk and reused in other sessions or pro-

grams.

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Figure 4: Simultaneous overlay of multiple visualization

techniques for one scanpath of an image viewing task. The

background image is shown together with a scanpath repre-

sentation of ﬁxed-size ﬁxation markers (small circles) and

generated ﬁxation clusters (bigger ellipses) for left (green)

and right (blue) eye. AOIs were annotated by hand (marked

as white overlay).

3.5 Scanpath Comparison

Various characteristics of the scanpaths such as ﬁxa-

tion durations and saccade lengths, as well as visual

attention distribution and glance proportion towards

ﬁxation clusters can be calculated and exported. In

addition to these global time-integrated scanpath de-

scriptors, it is also possible to automatically compare

scanpaths to each other. We therefore implemented

a variant of ScanMatch (Cristino et al., 2010) that

makes use of the ﬁxation clusters and areas of inter-

est described above. Fixation clusters are used in or-

der to label the scanpath data (Santella and DeCarlo,

2004). ScanMatch then tries to align the ﬁxation se-

quences by the Needleman-Wunsch string alignment

algorithm.

We are planning to extend the scanpath compari-

son capabilities with automated image segmentation

and object tracking functionality for AOI annotation

as well as probabilistic scanpath comparison metrics.

4 DATA VISUALIZATION

The software allows simultaneous visualization of

multiple scanpaths. These may represent different

subjects, subject groups or distinct experiment con-

ditions. The scan patterns are rendered in real-time

as an overlay to an image or video stimulus. Vari-

ous customizable visualization techniques are avail-

able: Fixations that encode ﬁxation duration in their

circular size, elliptical approximations encoding spa-

tial extend as well as attention and shadow maps. Ex-

ploratory data analysis can be performed by travers-

ing through the time dimension of the scan patterns as

if it was a video. Most of the visualizations are inter-

active so that placing the cursor over the visualization

of e. g. a ﬁxation gives access to detailed information

such as its duration and onset time.

4.1 Fixations and Fixation Clusters

The visualization of ﬁxations and ﬁxation clusters has

to account for their spatial and temporal information.

It is common to draw them as circles of either uniform

size or to encode the ﬁxation duration as the circle di-

ameter. Besides these options, Eyetrace2014 offers

an ellipse ﬁt visualization to the spatial extend of the

ﬁxation. The eigenvectors of all measurement points

assigned to the ﬁxation are calculated. These vectors

point into the direction of highest variance within the

data (see Figure 5). This visualization is especially

useful when evaluating ﬁxation ﬁlters and their pa-

rameters. We found it interesting and important to see

that the jitter within a ﬁxation does often not form a

circular, but a stretched ellipse.

Figure 5: The two eigenvectors of Gaussian distributed

samples (that correspond to the directions of highest vari-

ance). These are used as the major and minor axes for an

elliptic ﬁt.

4.2 Attention and Shadow Maps

Attention maps are one of the most common eye-

tracking analysis tools, besides the high number of

subjects that have to be measured in order to get reli-

able results (Pernice and Nielsen, 2009). In order to

enable fast attention map rendering even for a large

number of recordings and high resolution, the atten-

tion map calculation utilizes multiple processor cores.

Attention maps can be calculated for gaze points, ﬁx-

ations and ﬁxation clusters. We provide the classical

red-green color palette for attention maps as well as

blue version for color-blind persons.

For the gaze point attention map each gaze point

contributes as a two dimensional Gaussian distribu-

tion. The ﬁnal attention map is then the sum over

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(a) (b)

Figure 6: An attention map calculated for ﬁxation clusters

(a) and the corresponding shadow map (b).

all Gaussians. The Gaussian distribution is speciﬁed

by the two parameters size and intensity which are

adjustable by the user. This Gaussian distribution is

circular because gaze points do not have information

about orientation and size. For ﬁxations and ﬁxation

clusters the elliptic ﬁt is used to determine the shape

and orientation of the Gaussian distribution. Figure 7

shows an example of a circular Gaussian distribution

(a) and a stretched, elliptical one (b).

P(x, y) =

2π

√

1 −ρ

−

2(1−ρ

)

(

−

2ρxy

)

(1)

Equation 1 shows the Gaussian distribution in the

two dimensional case. σ

and σ

are the variance in

horizontal and vertical direction respectively (see Fig-

ure 7). x and y are the offsets to the center of the

Gaussian distribution (see Figure 7). The correlation

coefﬁcient ρ is zero in Figure 7 to simplify the case.

(a) (b)

Figure 7: Two Gaussian distribution calculated with σ

= 1 (a) and σ

= 1 and σ

= 5 (b).

A variant of the attention map is the shadow map

that reveals only areas that were looked at (see Fig-

ure 6(b)). Its calculation is identical to that of the at-

tention map with the difference of a smoothing step

in order to show the border regions with higher sen-

sitivity. This is done by calculating the n-th root of

each map value where n is a user-deﬁned parameter

that regulates the desired smoothing.

4.3 Saccades

Saccades are typically visualized as arrows or lines

connecting two ﬁxations.

Besides this, a statistical evaluation can be visu-

alized as a diagram called anglestar. It consists of a

number of slices and a rotation offset. A slice of the

anglestar codes in its length the number of saccades

with the same angular orientation as the slice (e.g. if

the slice represents the angles between 0

◦

and 45

◦

the

number of saccades within that angle range contribute

to that slice) to the horizontal axis is considered. The

extend of a slice from the center of the star can repre-

sent the quantity, summed length or summed duration

of the saccades towards that direction. Figure 8 shows

a diagram where the extension of the slices is based

on the summed length of the saccades.

Figure 8: Representation of an anglestar, the red part repre-

sents data of the left eye, the blue part refers to data of the

right eye.

4.4 AOI Transitions Diagram

For some evaluation cases it is interesting in which

sequence attention is shifted between different areas.

The AOI transitions diagram (Fig. 9) visualizes the

transition probabilities between AOIs during a spe-

ciﬁc time period. The color of the transition is in-

herited from the AOI with most outgoing saccades.

Hovering the mouse over an AOI shows all transitions

from this AOI and hides the transitions from all other

AOIs. Hovering the cursor over a speciﬁc transition

displays an information box containing the number of

transitions in both directions. Figure 9(b) e.g. shows

that after watching AOI cluster3 in more than 80%

of cases gaze stayed at cluster3, in some cases gaze

moved on towards cluster1 or cluster2 and in very few

cases towards cluster4 and cluster5.

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(a) (b)

Figure 9: Diagram of the transitions between AOIs. The

graphic is interactive and can blend out irrelevant edges if

one AOI is selected (b).

5 DATA EXPORT

5.1 Statistics

General Statistics

Independent of all other calculations it is possible to

calculate some general gaze statistics. These include

the horizontal and vertical gaze activity, minimum,

maximum and average speed of the gaze. These

statistics shine a light on the agility and exploratory

behavior of the subjects and can be exported in a

format ready to use in statistical programs such as

JMP or SPSS.

AOI Statistics

Numerous gaze characteristics can be calculated for

AOIs, such as the total number of glances towards the

AOI, the time of the ﬁrst glance, glance frequency, to-

tal glance time, the glance proportion towards the AOI

in respect to the whole recording and the minimum,

maximum and mean glance duration. These statistics

are a supplement to the AOI transitions diagram and

can also be exported.

5.2 Visualization

The transition diagram as well as every visualization

can be exported either loss-less as vector graphics or

as bitmaps (png, jpg). Eyetrace2014 provides the op-

tion to export the information about the subject (e.g.

age, dominant eye) and the parameters used for cal-

culation and visualization as a footer in the exported

image. That way results can be reproduced and un-

derstood based solely on the exported image.

5.3 Evaluation Results

After calculating ﬁxations, ﬁxation clusters or cumu-

lative clusters Eyetrace2014 provides the possibility

to export them as a text ﬁle.

Fixations are exported in a table including the run-

ning number, the number of included points, x and y

coordinate, radius and if calculated the id of the clus-

ter this ﬁxation belongs to. The text ﬁle for the clus-

ters and cumulative clusters include an ID number,

the number of ﬁxations contained, mean x, mean y

and the radius.

Besides the already mentioned text ﬁles, ﬁxations

saccades and measurement error sections can be ex-

ported in order to allow extensive further processing

in statistics programs, choosing the export option ”ex-

port events”.

6 CONCLUSION AND OUTLOOK

Summarizing our work of the last year and a half we

believe that Eyetrace2014 is a well structured pro-

gram, advantageous enough to use it for university

research but with its convenient handling nonetheless

usable for persons without broad eye tracking expe-

rience, e.g. for teaching students. The major advan-

tages of the software are the ﬂexibility of algorithms

and their parameters as well as their actuality in re-

spect to the state of the art.

Eyetrace2014 has already been employed in sev-

eral research projects, ranging from the viewing of

ﬁne art recorded via a static binocular SMI infrared

eye-tracker to on-road and simulator driving experi-

ments (Kasneci et al., 2014b; Tafaj et al., 2013) and

supermarket search tasks (Sippel et al., 2014; Kasneci

et al., 2014c) recorded via a mobile Ergoneers Dikab-

lis tracker.

Nevertheless there are many plans to extend the

Eyetrace software bundle in the next versions.

Besides the mandatory implementation of new

eye tracker models to EyetraceButler we will extend

the quality-check possibilities for the EyetraceButler

plug-ins.

We want to extend the general and AOI based

statistics calculations, add new calculation or visual-

ization algorithms and make the existing ones more

interactive and transparent. A special focus will be

given the analysis and processing of saccadic eye

movements as well as to the automated annotation of

AOIs for dynamic scenarios (K

ubler et al., 2014) and

non-elliptical AOIs.

We plan on including further automated scanpath

comparison metrics, such as MultiMatch (Dewhurst

et al., 2012) or SubsMatch (K

ubler et al., 2014).

Another relevant area is the monitoring of vigi-

lance and workload during the experiment. Especially

for medical applications such as reaction or stimu-

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lus sensitivity testing the mental state of the subject

is of importance. Available data such as the pupil

dilation, fatigue waves (Henson and Emuh, 2010),

saccade length differences (Di Stasi et al., 2014) and

blink rate may give important insight into the data and

even yield e.g. cognitive workload weighted attention

maps.

ACKNOWLEDGEMENTS

We want to thank the department of art history at

the university of Vienna, especially Johanna Aufreiter

and Caroline Fuchs for the inspiring collaboration.

The project was partly ﬁnanced by the the WWTF

(Project CS11-023 to Helmut Leder and Raphael

Rosenberg).

REFERENCES

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Brinkmann, H., Commare, L., Leder, H., and Rosenberg, R.

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