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The paper presents studies on the application of the dissim-ilarity matrix-based method to the eye movement analysis. This method was utilized in the biometric identification task. To assess its efficiency four different datasets based on similar scenario ('jumping point' type) yet using different eye trackers, recording frequencies and time intervals have been used. It allowed to build the common platform for the research and to draw some interesting comparisons. The dissimilarity matrix, which has never been used for identifying people on the basis of their eye movements, was constructed with usage of different distance measures. Additionally, there were different signal transforms and met-rics checked and their performance on various datasets was compared. It is worth mentioning that the paper presents the algorithm that was used during the BioEye 2015 competition and ranked as one of the top three methods.
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Using Dissimilarity Matrix for Eye Movement
Biometrics with a Jumping Point Experiment
Pawel Kasprowski and Katarzyna Harezlak
Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
pawel.kasprowski@polsl.pl
Abstract. The paper presents studies on the application of the dissim-
ilarity matrix-based method to the eye movement analysis. This method
was utilized in the biometric identification task. To assess its efficiency
four different datasets based on similar scenario (’jumping point’ type)
yet using different eye trackers, recording frequencies and time inter-
vals have been used. It allowed to build the common platform for the
research and to draw some interesting comparisons. The dissimilarity
matrix, which has never been used for identifying people on the basis of
their eye movements, was constructed with usage of different distance
measures. Additionally, there were different signal transforms and met-
rics checked and their performance on various datasets was compared.
It is worth mentioning that the paper presents the algorithm that was
used during the BioEye 2015 competition and ranked as one of the top
three methods.
Keywords: eye movement biometrics, dissimilarity matrix, fusion, dy-
namic time warping
1 Introduction
Eye movement biometrics has been investigated for over 10 years, however there
are still no commercial applications utilizing this modality. The main problem is
lack of established and well understood methods that can be used to distinguish
eye movement characteristics of different people.
Some effort has already been made to solve this problem. There are eye
movement datasets available to download, and there are biometric contest or-
ganized like EMVIC 2012 [6], EMVIC 2014 [5] or BioEye 2015 [13]. But the
methods used by contest participants are not always published and therefore are
sometimes not reproducible. Moreover, because multiple submissions are pos-
sible during such a competition and typically some training data is available
in advance, the methods are optimized for the competition’s dataset and suffer
from poor generalization on other datasets. Such a generalization requires the
application of elaborated methods for datasets collected using different setups
and for different users, which may help to find out a solution serving well for
many eye movements collections.
This is a pre-print. The final version of the paper was published in Springer, Smart Innovation,
Systems and Technologies, Vol. 57, 2016 as part of Proceedings of the 8th KES International
Conference on Intelligent Decision Technologies (KES-IDT 2016) – Part II and is available in
Springer Link Library: https://link.springer.com/chapter/10.1007/978-3-319-39627-9_8
This was the one of the motivating factors to apply the new feature extraction
method – which was developed by authors for the BioEye 2015 competition and
ranked as one of the top three methods – to various eye movement datasets. This
method, based on dissimilarity matrix [2], has not yet been used for eye move-
ment biometrics. We checked its performance using various signals, transforms
and divisions of samples.
To overcome the lack of generalization, the usefulness of the solutions applied
was tested on four different datasets recorded using three different eye trackers.
It enabled us to draw some meaningful conclusions about efficiency of various
approaches combinations and experiment’s scenarios.
2 Eye Movement Biometrics Using a ’jumping point’
Stimulus
All datasets which were used, were recorded using a jumping point - one of the
most popular stimuli. During such an experiment a subject is instructed to follow
with eyes a point displayed on a screen. The point’s position changes periodically
- that is why it is called a ’jumping point’. The advantage of such a stimulus is
that eye movements are more or less predictable and comparable between trials.
On the other hand such a stimulus forces a specific behavior so it measures more
physiological patterns of a person than behavioral ones.
The first usage of such a kind of stimulus was reported in [7]. There were
cepstral coefficients used as features for a classifier. In the work [8] the idea
was extended with usage of Principal Component Analysis (PCA) to reduce
the number of attributes. Another notable work was [10]. Authors extracted
saccades and used training samples to create an Oculomotor Plant Mathematical
Models (OPMM) [12]. The idea was extended in [11] where nine oculomotor plant
characteristics (OPC) were empirically chosen. The OPC biometrics calculated
for different subjects were compared using a voting version of Student t-test
and the Hotelling T-square test. The results were fused using logical AND or
OR techniques.
In 2012 there was the first Eye Movement Verification and Identification
Competition (EMVIC) organized and it resulted in several publications [6].
There were four datasets presented - all created using a ’jumping point’ stimu-
lus. According to [6] the winner of the competition divided samples into parts
and calculated 2D histograms of speed and direction. The second place holder
extracted velocity and acceleration and compared their distributions using the
multivariate Walf-Wolfowitz test [14].
In 2013 Holland and Komogortsev [3] compared results for different stimuli
and devices using the same set of 14 features (named CEM features). The results
were calculated for every feature and the fusion of all features.
Finally, in 2015 there was the BioEye competition announced with four
datasets [13]. Two of them were based on jumping point stimulus and were
used in the presented research.
3 Feature Extraction and Classification
Before any feature extraction method was applied to eye movement signal, each
sample from the dataset was divided into events. An event was a part of a sample
for which stimulus point’s position was in the same place. Every event was
described by a starting position - location of the point just before the event -
and the ending position - location of the point during the event. A direction of
an event was defined as a direction of a vector from its start position to end one.
Signals Extraction. On the basis of the raw eye positions, the first, second
and third derivatives were extracted for every event independently. There were
velocity (v), acceleration (a) and jerk (j) calculated as an absolute value and
for both horizontal and vertical directions. It resulted in 9 signals for every
event (Table 1).
Table 1. Set of signals extracted from eye movement
signal formula description
vx, vy Vx=∂x
∂t , Vy= y
∂t the first derivative of xand y
(i.e. vertical and horizontal velocities)
vxy V=V2
x+V2
ythe first derivative for absolute velocity
ax, ay V
x=∂Vx
∂t , V
y=∂Vy
∂t the second derivative of xand y
(i.e. vertical and horizontal accelerations)
axy V
=V2
x+V2
ythe derivative of vxy
jx, jy V
′′
x=∂V
x
∂t , V
′′
y=∂V
y
∂t the third derivative of xand y(jerk)
jxy V
′′ =V′′2
x+V′′2
ythe derivative of axy
where x,y- the raw coordinates
Signal Transformation. The next phase was the calculation of different trans-
forms from each of the nine signals separately. There were four transforma-
tions used: Fourier transform (F) ([8]), Cepstrum transform (C) ([7]) Daub
Wavelet transform (W) and signal normalization to 0-1 (N). Together with
not transformed signal (S) it gave 5 different transforms and altogether, there
were 9 ×5= 45 different signals extracted.
Features Preparation. Signals obtained in the previous pre-processing phase
were subsequently used to build feature sets with usage of the dissimilarity
matrix-based method [2]. Similar method has already been used for behavioral
biometrics [16], but using it for eye movement signal is our original contribution.
While preparing feature sets, at first the eye movement dataset was divided by
half into training and testing events. Because each of datasets used to evaluate
the proposed method was built from samples collected during two sessions, there
were always two samples for each subject. Events from the first user’s sample
were treated as training, while events from the second sample as testing ones.
Then, for every training event, its distances to all other training events were
calculated. These distances formed a feature set, consisting of N features where
N is the number of training events. This feature set was used as an input to
a classification model building algorithm. The same procedure was used for test-
ing events - at first distances to all training events were calculated and formed
a set of attributes and then this set was used to classify the given event.
Given two signals Saand Sbthe distance between them may be calculated
using different measures. In this research three of them were taken into account
– Dynamic Time Warping (DTW) [1], Euclidean distance (EUC) and Earth
Mover’s Distance (EMD) [15]. As a result, a separate feature set for every com-
bination of every signal and distance measure was prepared (45 ×3 = 135
feature sets).
Classification. As it was stated above, every dataset used consisted of two
sessions for each subject. During the classification, events from the first session
of the subject were always used as training data and the events from the second
session of the same subject as testing data. The K Nearest Neighbors algorithm
with value of K equal to 1 was used as a classifier and every testing event was
classified separately.
The final result for each testing sample was determined using a simple score
fusion. The classification model, for every event e, returned a probability p(e, c)
that this event belongs to class c. For 1NN classifier this value was equal to 1
for one class and 0 for all other classes. Score for class cin every sample swas
calculated as: score(s, c) = E
e=1 p(e, c),where Emeans all events belonging
to the sample s. The final label for the sample was calculated as: label(s) =
argmaxc(score(s, c))
4 Datasets
The studies discussed in the paper were conducted using four datasets called
JAZZ, VOG, RAN30 and RAN1Y. As it was mentioned above all of them were
based on a jumping point stimulus, however the time of presentation and a num-
ber of point’s positions displayed differed for given sets. Other differences re-
garded a type of an eye tracker used to record eye movements, numbers of users
taking part in experiments and a time interval between sessions of an experiment.
The detailed information for each set is provided below.
VOG dataset - VOG dataset was obtained with usage of the self-developed
VOG head-mounted eye tracker with a single CMOS camera with USB 2.0 in-
terface (Logitech QuickCam Express) possessing 352x288 sensor and lens with
IR-Pass filter. The camera was mounted on the arm attached to head and was
pointing at the right eye. The system generated 20 - 25 measurements of a center
of a pupil per second. The dataset consisted of recordings collected for 26 partic-
ipants during two sessions separated by three weeks interval. One recording of
an eye movement referred to 30 points displayed on a screen, each for 3 seconds.
There were 52 recordings in this dataset each including 1400-1500 samples.
JAZZ dataset - The second dataset was obtained using head mounted Jazz-
Novo eye tracker (product by Ober-consulting) that records eye positions with
frequency 1000Hz. It uses direct Infra-Red Oculography (IROG) and utilizes
pairs of IR emitters and sensors. The optoelectronic transducers are located
between the eyes. This set included 48 recordings from two sessions related to 24
participants. A setup for a between session interval, a number and time of stimuli
displayed was the same like for the VOG dataset. Each recording consisted of
between 99000 to 100000 samples.
RAN30 and RAN1Y datasets - Both RAN30 and RAN1Y datasets were
part of the BioEye competition and were recorded using an EyeLink eye-tracker
working at 1000 Hz. The raw eye movement signals were subsampled to 250 Hz
with the usage of an anti-aliasing filter.
RAN30 dataset was built on the basis of recordings of 153 subjects and was
collected during two sessions organized one by one in 30 minutes (all together
306 recordings). During each session user’s task was to follow with eyes 100
points, each of which was shown for one second, which gave 25000 samples for
one recording.
RAN1Y dataset consisted of recordings of 37 subjects. The only difference
between RAN30 and RAN1Y experiments was the interval between sessions - it
was one year in the latter.
5 Comparison of Results
Results obtained from the classification process were studied in terms of an in-
fluence of pre-processing phases on a final accuracy of a classification. For this
analysis purpose ANOVA test was used to check an existence of significant dif-
ferences among groups of the above described feature creating methods. In case,
when such a difference was found, Tukey’s HSD test was applied to determine,
which groups exactly differ from each other. Comparing these outcomes with an
accuracy of a classification results allowed us to point out method yielding the
best results.
In the first step of the analysis the transform type applied to each kind
of signals was taken into account. In all four analyzed datasets results of the
ANOVA test rejected the null hypothesis, that all groups had identical means.
Deeper studies of differences with usage of Tukey HSD test and classification
results revealed that for all sets Wavelet and Cepstrum transforms gave signifi-
cantly worse results than three other types - normalization (N), Fourier (F) and
original signal (S). The latter group (S, N, F) provided better accuracy, however
these results turned to be not significant between each other, with one exception,
the VOG set.
Subsequently, our attention was paid to measures used for calculating dis-
similarity matrix. As it was mentioned above, there were three different distance
measures: Dynamic Time Warping, Euclidean distance and Earth Mover Dis-
tance (denoted during tests by D, E, M respectively) taken into account. The
comparison of these methods using the ANOVA test in conjunction with stud-
ies of the classification accuracy revealed that for all four datasets DTW pro-
vided the best classification results. The statistical significance was confirmed
for RAN30 and RAN1Y, and in regard to EMD method in VOG data set. Only
in case of JAZZ set statistically significant differences were not found. All these
discussed results are collected in Figure 1.
It can be seen that for video based eye trackers the results are correlated
with frequency sampling as the results for RAN30 and RAN1Y datasets are
significantly better than for VOG dataset recorded with much lower frequency.
On the other hand, the results of the same method for Jazz-Novo eye tracker
- working differently - are worse, despite of its very high recording frequency
(1000Hz). It shows that the method presented in this paper is not sufficient for
such kind of data.
M
M
M
M
E
E
E
E
D
D
D
D
-0,05
0,05
0,15
0,25
0,35
0,45
0,55
VOG JAZZ RAN30 RAN1y
Fig. 1. Mean results of classification for all four sets and every distance (D - DTW dis-
tance, E - Euclidean distance, M - EMD distance). Significant differences were marked
with horizontal lines
6 Fusion of Feature Sets and Final Results
To check the real strength of that method to perform users’ identification it was
decided to combine feature sets results in a score level voting fusion. For every
feature set at first a score for each sample sand class cwas determined and then
a score for fusion was calculated as: scorefus(s, c) = K
k=1 scorek(s, c) where K
is the number of feature sets taken into account. The final label for a sample s
was determined based on equation labelfus (s) = argmaxc(scoref us (s, c))
The aim of this analysis was to find a combination of features, which provides
the best classification results for all datasets. Because Wavelet and Cepstrum
transforms gave in most cases results significantly worse than the other trans-
forms, and Earth Mover Distance was the worst among distances - feature sets
prepared using these pre-processing methods were omitted in the subsequent
analysis. Additionally, because Dynamic Time Warping gave the best results for
every dataset, it was decided to use feature sets based on this metric in every
analyzed combination. As the result, only three transforms (S, N and F) and
two distance measures (DTW and EUC) were taken into account. There were
different combinations of feature sets checked with a number of feature sets rang-
ing from 27 to 162. The results were also compared with the combination of all
feature sets (see Table 2).
Table 2. Accuracies and Equal Error Rates obtained for different combinations of
transforms and distance functions.
transf./distance Accuracy EER
RAN30 RAN1Y VOG JAZZ RAN30 RAN1Y VOG JAZZ
SFNCW/DEM 81,1% 56.8% 34,6% 12.5% 6.5% 18.1% 30.8% 41.7%
S/D 81.1% 51.4% 38.5% 8.3% 8.1% 24.3% 35.1% 39.6%
N/D 78.4% 54.1% 15.4% 8.3% 9.4% 17.8% 30.8% 45.4%
F/D 70.3% 51.4% 15.4% 16.7% 9.3% 18.9% 38.5% 42.2%
SN/D 83.8% 54.1% 34.6% 8.3% 6.4% 19.8% 33.2% 38.7%
SF/D 78.4% 56.8% 30.8% 12.5% 8.1% 21.6% 38.5% 41.7%
FN/D 78.4% 59.5% 23.1% 12.5% 8.1% 16.8% 36.4% 41.9%
SFN/D 81.1% 62.2% 34.6% 8.3% 6.1% 19.6% 35.6% 42.8%
S/DE 89.2% 54.1% 15.4% 8.3% 8.1% 20.9% 31.5% 41.7%
N/DE 73.0% 54.1% 19.2% 12.5% 9.8% 16.2% 33.5% 41.7%
F/DE 75.7% 48.6% 15.4% 12.5% 9.5% 20.1% 40.0% 37.5%
SN/DE 83.8% 59.5% 26.9% 8.3% 5.8% 18.5% 31.2% 39.8%
SF/DE 83.8% 59.5% 34.6% 8.3% 8.1% 18.9% 35.1% 39.6%
FN/DE 83.8% 54.1% 26.9% 8.3% 6.2% 17.2% 35.7% 45.2%
SFN/DE 89.2% 59.5% 26.9% 8.3% 5.4% 18.2% 33.4% 40.7%
S - not transformed signal, F - Fourier, N - Normalization, C - Cepstrum,
W - Wavelet, D - DTW distance, E - Euclidean distance, M - EMD distance
The obtained results were examined in terms of an existence of Pearson
correlation between datasets. These studies confirmed it for RAN30 and RAN1Y
results (0.42) and for RAN1Y and VOG (0.43). The correlation between VOG
and RAN30 results is lower but still visible (0.37). What is interesting, the results
for JAZZ dataset are negatively correlated with VOG (-0.49). The main reason
of this fact is that, contrary to VOG, JAZZ dataset gave quite good results for
Fourier based transform.
The best combination of feature sets was SF N and D - three transforms: (S)
not transformed signal, (F) Fourier, (N) Normalization and: (D) DTW distance
measure - with 46.55% accuracy on average for all datasets. However, it is visible
that differences in classification accuracy among datasets are significant and the
results are reasonable only for RAN30 and RAN1Y datasets.
Additionally, false rejection and false acceptance rates for Rnumber of tested
recordings and different acceptance thresholds th were calculated using equations
(1) and (2).
F RR(th) = RR
s=1 as,c(s)
R(1)
F AR(th) = R
s=1 C
j=1,j̸=c(s)as,j
(C1)R(2)
where c(s) denotes the class identifier the sample sbelongs to and as,j is given
by Eq. (3):
as,j (th) = {1scoref us(s, j )> th
0otherwise (3)
By changing the acceptance threshold, the Equal Error Rate (EER) - error
value for the threshold for which FAR and FRR are equal - was calculated for
each set. The results are presented in Table 2. The best combination is the same
as for accuracy only for RAN30 dataset, there are differences in all other datasets.
While accuracy measure deals only with one - the best - result, EER calculation
takes into account all results so it may be treated as a better description of
model’s performance. Results obtained for RAN30 and RAN1Y datasets are
acceptable as for eye movement biometric - in fact EER equal to 5.4% is one of
the best results published so far. However, the same method used for VOG and
JAZZ datasets achieved significantly higher error rates.
7 Discussion
The primary aim of the experiments presented in this paper was to examine
whether usage of exactly the same method for various datasets of eye move-
ments would ensure the similar classification efficiency in all considered cases.
It occurred that the results obtained for each of the four datasets used differed
substantially in accuracy, however there were some common patterns visible,
when comparing performance of different transforms and distance measures.
The detailed discussion of these outcomes is provided below.
Analyzing results concerning distance measures it turns out that Dynamic
Time Warping method proved to be the best choice for every dataset while
Earth Mover’s Distance function was the worst one. Additionally the usage of
Wavelet and Cepstrum transforms did not offer any improvement to the results
in any dataset.
The differences in the accuracy and EER for the same method and different
datasets show that every new method to be applied for eye movement based iden-
tification - despite of achieving good results for some available dataset - should
always be checked against other data collections before any general conclusions
about its performance may be presented.
Another issue to explore was to check, which properties of datasets influenced
the results. All datasets used were built using a very similar scenario (a jumping
point stimulus). Therefore, it was possible to compare results directly. Two of
the datasets (RAN30 and RAN1Y) were collected with usage of the same equip-
ment and the correlation of results for these two datasets is visible. The results
are also correlated with the VOG dataset, which was created with a similar tech-
nique using infrared camera (however with much lower frequency). Interestingly,
it turned out that the results for the last of the datasets (JAZZ) are completely
different and the correlation is even negative. As the latter dataset was gathered
using a device utilizing a completely different technique (IROG) it may be sup-
posed that a type of a device utilized to record data has a significant influence
on classification results.
The other interesting conclusion may be the finding, that a pool of partici-
pants did not influence results significantly. There were different pools used for
RAN30 and RAN1Y datasets and results were similar while almost the same
pool of participants was used for both VOG and JAZZ datasets - and the results
were different in this case.
Comparing RAN30 and RAN1Y datasets it is visible that, despite of similar
distribution of the results, the results for RAN30 dataset are significantly better.
The only reason for this may be a different time interval between sessions, which
was 30 min for RAN30 and 1 year for RAN1Y dataset. It shows that short term
repeatability of eye movement caused by current attitude or mood of a person
may significantly (and artificially) improve classification results. It is in line with
conclusions derived in [4] and [9].
8 Summary
The studies conducted in this research were inspired by the awareness that the as-
sessment of methods used for eye movement data processing and analysis should
be done by their comparison with other studies conducted in the same field.
Having different collections of data it is possible to explore an influence of
some data pre-processing methods on the final classification result, which was
presented in the paper. The research confirmed the existence of both some dif-
ferences and some patterns when various methods and results obtained for them
are taken into account. It allows to suppose that continuing such a type of stud-
ies will enable to reach some general conclusions in field of eye movement data
biometrics. Because the results presented in this work are far from perfect it
indicates that there is a lot of work to be done to lower error rates.
Acknowledegement. The authors would like to thank organizers of BioEye
2015 competition for publishing eye movement datasets that were used in this
research. We also acknowledge the support of Silesian University of Technology
grant BK/263/RAu2/2016.
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In this paper, the biometric potential of eye movement patterns extracted from low frame rate eye-tracking devices is evaluated. Also, possible improvement in recognition rates is investigated using other static and dynamic features extracted from the eyes including eye blinking patterns and periocular shape features. These modalities can be applicable for specific biometric applications like continuous driver authentication for law enforcement. For this purpose, two databases are collected with two low frame rate eye-tracking systems that capture the eye movements. Data were recorded from 55 participants while watching real driving sessions. For eye gaze, features from fixations and saccades are extracted separately including duration, amplitude, and statistical features. For eye blinking, features from the blinking pattern, its speed, acceleration, and power per unit mass profiles are extracted. Periocular features include the eye-opening height, width, axial ratio, etc. Each modality is evaluated first, then, these modalities are combined in a multi-modal setup for performance improvement. While each trait achieved a moderate performance in a single-modality setup, the fusion of the static and the dynamic features from the eye provides a great performance improvement up to 98.5% recognition rate and 0% error rate in the two modes of authentication. Although the single-modality setup might not be secure enough, the fusion of these traits achieves high levels of identification making these traits effective for continuous driver authentication application.
Preprint
Person identification based on eye movements is getting more and more attention, as it is anti-spoofing resistant and can be useful for continuous authentication. Therefore, it is noteworthy for researchers to know who and what is relevant in the field, including authors, journals, conferences, and institutions. This paper presents a comprehensive quantitative overview of the field of eye movement biometrics using a bibliometric approach. All data and analyses are based on documents written in English published between 2004 and 2019. Scopus was used to perform information retrieval. This research focused on temporal evolution, leading authors, most cited papers, leading journals, competitions and collaboration networks.
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Person identification based on eye movements is getting more and more attention, as it is anti-spoofing resistant and can be useful for continuous authentication. Therefore, it is noteworthy for researchers to know who and what is relevant in the field, including authors, journals, conferences, and institutions. This paper presents a comprehensive quantitative overview of the field of eye movement biometrics using a bibliometric approach. All data and analyses are based on documents written in English published between 2004 and 2019. Scopus was used to perform information retrieval. This research focused on temporal evolution, leading authors, most cited papers, leading journals, competitions and collaboration networks.
Conference Paper
The article presents the results of the optimization process of classification for five selected data sets. These data sets contain the data for the realization of the multiclass classification. The article presents the results of initial classification, carried out by dozens of classifiers, as well as the results after the process of adjusting parameters, this time obtained for a set of selected classifiers. At the end of article, a summary and the possibility of further work are provided.
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The idea concerning usage of the eye movement for human identification has been known for 10 years. However, there is still lack of commonly accepted methods how to perform such identification. This paper describes the second edition of Eye Movement Verification and Identification Competition (EMVIC), which may be regarded as an attempt to provide some common basis for eye movement biometrics (EMB). The paper presents some details describing the organization of the competition, its results and formulates some conclusions for further development of EMB.
Conference Paper
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Biometric recognition via eye movement-driven features is an emerging field of research. Eye movement cues are characterized by their non-static nature, the encapsulation of physical and behavioral traits, and the possibility to be recorded in tandem with other modalities, e.g. the iris. The BioEye 2015 competition was organized with the aim to boost the evolution of the eye movement biometrics field. The competition was implemented with a particular focus on the issues facing the researchers in the domain of the eye movement recognition, e.g. quality of the eye movement recordings, different visual stimulus types, and the effect of template aging on the resulting recognition accuracy. This paper describes the details and the results of the BioEye 2015 competition, which provided the largest to date biometric database containing records from 306 subjects, stimulus of two types, and recordings separated by short-time and long-time intervals.
Conference Paper
Full-text available
The idea concerning usage of the eye movement for human identification has been known for 10 years. However, there is still lack of commonly accepted methods how to perform such identification. This paper describes the second edition of Eye Movement Verification and Identification Competition (EMVIC), which may be regarded as an attempt to provide some common basis for eye movement biometrics (EMB). The paper presents some details describing the organization of the competition, its results and formulates some conclusions for further development of EMB.
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This paper presents an objective evaluation of previously unexplored biometric techniques utilizing patterns identifiable in human eye movements to distinguish individuals. The distribution of primitive eye movement features are compared between eye movement recordings using algorithms based on the following statistical tests: the Ansari-Bradley test, the Mann-Whitney U-test, the two-sample Kolmogorov-Smirnov test, the two-sample t-test, and the two-sample Cramer-von Mises test. Score-level information fusion is applied and evaluated by: weighted mean, support vector machine, random forest, and likelihood ratio. The accuracy of each comparison/jusion algorithm is evaluated, with results suggesting that, on high resolution eye tracking equipment, it is possible to obtain equal error rates of 16.5% and rank-1 identification rates of 82.6% using the two-sample Cramér-von Mises test and score-level information fusion by random forest, the highest accuracy results on the considered dataset.
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This paper presents the results of the first eye movement verification and identification competition. The work provides background, discusses previous research, and describes the datasets and methods used in the competition. The results highlight the importance of very careful eye positional data capture to ensure meaningfulness of identification outcomes. The discussion about the metrics and scores that can assist in evaluation of the captured data quality is provided. Best identification results varied in the range from 58.6% to 97.7% depending on the dataset and methods employed for the identification. Additionally, this work discusses possible future directions of research in the eye movement-based biometrics domain.
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Eye movements’ identification is an interesting alternative to other biometric identification methods. It compiles both physiological and behavioral aspects and therefore it is difficult to forge. However, the main obstacle to popularize this methodology is lack of general recommendations considering eye movement biometrics experiments. Another problem is lack of commonly available databases of eye movements. Different authors present their methodologies using their own datasets of samples recorded with different devices and scenarios. It excludes possibility to compare different approaches. It is obvious that the way the samples were recorded influences the overall results. This work tries to investigate how one of the elements – temporal proximity between subsequent measurements – influences the identification results. A dataset of 2556 eye movement recordings collected for over 5 months was used as the basis of analyses. The main purpose of the paper is to identify the impact of sampling and classification scenarios on the overall identification results and to recommend scenarios for creation of future datasets.
Conference Paper
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This paper explores some aspects that are involved during the construction of reliable benchmark sample databases for novel behavioral biometric identification methods, such as the data quality, the recording patterns and the post processing procedures that may be applied on the data. A large collection of eye movement samples was employed as a test case. It was recorded under a variety of settings and processed with a number of different approaches. Our analysis reveals that there are specific features during the construction of a database that may significantly influence the final identification performance. It also leads on the establishment of some guidelines, which can be generalized on other behavioral biometric methods, regarding the factors that should be taken into consideration during the creation, the description and the processing of a database of biometric samples.
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Behavior-based user authentication with pointing devices, such as mice or touchpads, has been gaining attention. As an emerging behavioral biometric, mouse dynamics aims to address the authentication problem by verifying computer users on the basis of their mouse operating styles. This paper presents a simple and efficient user authentication approach based on a fixed mouse-operation task. For each sample of the mouse-operation task, both traditional holistic features and newly defined procedural features are extracted for accurate and fine-grained characterization of a user's unique mouse behavior. Distance-measurement and eigenspace-transformation techniques are applied to obtain feature components for efficiently representing the original mouse feature space. Then a one-class learning algorithm is employed in the distance-based feature eigenspace for the authentication task. The approach is evaluated on a dataset of 5550 mouse-operation samples from 37 subjects. Extensive experimental results are included to demonstrate the efficacy of the proposed approach, which achieves a false-acceptance rate of 8.74%, and a false-rejection rate of 7.69% with a corresponding authentication time of 11.8 seconds. Two additional experiments are provided to compare the current approach with other approaches in the literature. Our dataset is publicly available to facilitate future research.
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The last few years a growing research interest has aroused in the field of biometrics, concerning the use of brain dependent characteristics generally known as behavioral features. Human eyes, often referred as the gates to the soul, can possibly comprise a rich source of idiosyncratic information which may be used for the recognition of an individual’s identity. In this paper an innovative experiment and a novel processing approach for the human eye movements is implemented, ultimately aiming at the biometric segregation of individual persons. In our experiment, the subjects observe face images while their eye movements are being monitored, providing information about each participant’s attention spots. The implemented method treats eye trajectories as 2-D distributions of points on the image plane. The efficiency of graph objects in the representation of structural information motivated us on the utilization of a non-parametric multivariate graph-based measure for the comparison of eye movement signals, yielding promising results at the task of identification according to behavioral characteristics of an individual.