welcome and thank you for your interest
in our paper
on behalf of my co-authors mark
abdelaziz lukas mecke
jonas auda uwe gruenefeld
florian alt and stefan schneegass i will
present to you our work with the title
understanding user identification in
virtual reality through behavioral
biometrics and the effect of body
normalization
applications for virtual reality often
require to know their user's identity
this is necessary as it allows the
application to personalize the
experience
by loading or setting personal
preferences or granting access to
personal information
current approaches to identify users
employ authentication schemes
such as pins or passwords that are
entered through handheld controllers and
a virtual keyboard
previous work has shown that these
traditional forms of authentication are
vulnerable to observation from a
bystander
who can easily spy on the hand gesture
of the immersed vr user
also traditional authentication schemes
that are currently widely used
need yet another password to be
remembered and interrupt the experience
throughout the password prompt
in our work we alternatively employ
behavioral biometrics in combination
with two task-based scenarios
to identify the users of our vr
application
our scenarios mimic common vr activities
such as bowling and archery
and are probably only two of many
possible examples
users interact naturally with the
scenarios and transparently in the
background
the spatial data of the handheld
controllers and the head mounted display
are captured by an identification system
the capture data is highly individual
and it allows to determine the identity
of the user through a deep learning
classifier
at this point it is important to note
that these bowling or archery scenarios
are not intended as a drop-in
replacement for a password prompt
instead they enable an implicit
interaction with an identification
scheme
where users behave naturally with the
task but provides the required spatial
data
for the identification scheme on the fly
we moreover seek to better understand
the role of behavioral biometrics in
user identification
and especially the connection to
physiologic biometrics within them
a movement that intends to solve a task
is first of course a form of behavior
thus falls into behavior biometrics but
the capabilities to move are also partly
defined by for example
the length of one's arms which means
that physiology also plays a role
virtual reality gives us the chance to
explore this relationship
as we try to eliminate the influence of
physiologic biometrics by imposing a
body normalization in vr
to do so we impose a height
normalization where each user has the
same virtual height of 168 centimeters
shorter or taller persons subsequently
have their virtual height reduced or
enlarged to match this exact value
we proceed in the same fashion with the
arms of each user
which is set to a span of 70 centimeters
measured from the front of the head
to the hand a combination of both is
also possible
where both the arms and the height is
normalized
we evaluated our approach in a lab-based
user study using a repeated measures
design
the study took place in two sessions
where each session was conducted on a
different day
our independent variables were first
scenario with two levels bowling vs
archery
and second type of normalization with
four levels following a counterbalanced
design
we acquired 16 participants and tested
them for both activities for 12
repetitions per body normalization
on a standard oculus quest device
afterwards we created multiple feature
sets and trained recurrent neural
networks and multi-layer perceptrons
with the data obtained from the first
day of the study we validated the models
exclusively with data from the second
day
each validation was performed per
activity and per type of normalization
so that only data of the same kind was
compared in total we find a validation
accuracy
of up to 90 for archery and 68
for the bowling activity we also found
several significant increases in the
identification rate
that were imposed by the body
normalizations where especially height
normalization had a large impact
we also tested the acquired scores in
the activities if the body normalization
had any effect on performance
and could not find significant
differences there further we asked the
participants if they felt any change and
only two participants mentioned this on
their own during bowling and another two
during archery
more participants realized the changes
once we asked more detail
but only less than half of them realize
the changes at all
to conclude the data that can easily be
collected with a standard of the shelf
hmd is suitable for identification and
contains highly specific information
when utilizing deep learning data
normalization is a best practice
a body normalization can further
increase this effect and boost the
identification accuracy significantly
moreover we have not seen a significant
difference in user performance
imposed by the body normalizations which
could be perceived only by less than
half of our participants
thank you for your attention