Hi, my name is Christoph Schröder and I'm from the University of Bremen. In our work
my colleagues and I analyzed how varying stimuli and trajectory lengths influence
the classification accuracy for eye movement biometrics. In eye movement
biometrics, we try to identify people solely on the way they gaze ofn things.
On the right-hand side, you can see that two users that read the same poem have
vastly different gaze patterns. The same is true when the task is not to read a
poem, but to follow a random moving dot on a screen. Eye movement biometrics have some
nique advantages. First we have an automatic liveness check. Second we can
use it for seamless and continuous authentication in that the user isn't
required to do a special authentication step but is continuously authenticated
while he uses his normal software. And last we can use it in HMDs. Actually,
there are already consumer grade HMDs where eye tracking is integrated
for for example for foveated rendering. Previous work can be split into three
major areas. First there was the bio-eye competition by Rigas et al. in 2017
where seven participants competed to achieve the highest accuracy in
identifying 153 users solely by gays patterns. This was task dependent in that
the users were either reading a poem or following a random dot. The winning entry
by George et al. achieved an astonishing accuracy of 82 to 84 percent. Another
area is task independent person authentication. Here, for example in the
work by Kimmunen et al. from 2010 the user isn't required to read a poem
but can do several things. Kimmunen et al. use gaussian mixture models to build
a background model for general gaze patterns, and then a model for each user in
regard to this background model. In other work is by Darvish et al. from 2013
where they use random forests and also identify 17 users with an accuracy of
37%. A last area is multimodal biometrics. Here for example in the
work by Pfeuffer et al. from 2019 18 users were recorded and not only their gaze
patterns but also their head movements and typing behaviors were used to
differentiate between them with a support vector machine. With only
the view based features an accuracy of 24% was achieved. As we can see in
previous works the evaluation was either very task dependent or the number of
users in the studies was quite small. In this work, we present three major
contributions. First we extend the current state of the art classifier by
George et al. by adding more features to increase it’s
accuracy and second we use random forests to increase the robustness in
the task independent setting. Second we analyze how using a different stimulus for
testing than it was used for training influences the identification accuracy.
Last we analyze how the length of the training and testing trajectory
influences the accuracy. This is especially important for the application
where the user doesn't want to wait too long until the authentication is
completed. Let me now introduce our identification pipeline. As input we get
a trajectory, which is mostly encoded in gaze angles. We then split the trajectory
into saccades and fixations. A fixation is a point where the user looks at for a
short amount of time and a saccade is a rapid eye movement that goes from
one fixation to another fixation. We can identify these two different parts by
looking at the velocity and using the IVT algorithm. For each saccade and
each fixation, we built an individual feature vector. And these vectors are
then fed into the classifier. Actually, we use two classifiers
One classifier predicts the user for each saccade and the other one for each fixation. This is
important as the saccades and fixations have vastly different feature scalings.
The predictions are then accumulated to get a finer prediction for the
trajectory. In our work, we found that averaging the prediction works quite
In this work we use two different classifiers. First we re-implement
the radial based function networks by Gerorge et al. Here in the hidden layer, we have
C times K in neurons where C is the number of users and K is an arbitrary
constant. In our work we found 32 to be good default. As activation function,
radial based functions are used as the name implies. For these we have to
estimate two parameters: mu is estimated by doing a k-means clustering where we
find 32 clusters for each user. From there we can estimate Sigma by just
taking the mean euclidean distance to the cluster centers. In a final step we
have to weight the output from the hidden layer to get the prediction
probabilities for each user in the output. The weighting is done by
calculating the more penrose pseudoinverse to minimize the
classification error. As a second classifier we use random decision
forests as the generalization ability is well proven. In our work we use all the
default settings from scikit learn but increase the number of trees to 400 to
account for the number of features and samples. For the features we start with
the same set of features as they were used by George at al. They proposed an
iterative feature selection scheme to find the best features for the fixations
and saccades. They also made it depend on the input stimulus and then found 9
features for fixations and 43 to 40 for the saccades. In our work we found that
when we use all features regardless of whether it's a fixation or a saccade
or the stimulus is text or random dot, the algorithm works better. In our first experiment we
confirmed that our re-implementation achieves the same accuracy as the
original algorithm by Georgia et al. Then we analyze whether using random
decision forests improves the performance but found that it actually
performs slightly worse. We also found that the stimulus whether it's text or
a moving dot doesn't have much of an impact on the accuracy. When we use all
features and omit the feature selection step we found that for all
configurations the accuracy increases. This shows that at least in our setting
both radial basis function networks and random decision forests work better when
no feature selection step was performed. In a second experiment we train on one
stimulus like the random moving dot and evaluate on the data where the
participants read the poem. In general we find that the maximum accuracy drops
significantly from before 94% to now 23%. We further found that the performance is
highly asymmetric, That is, when we train on random dot and evaluate on the
poem the performance is much worse than the other way around where we train on the
poem and evaluate on text. Further, we find that the random decision forest
this times generalizes better than the radial base function network. In another
experiment we analyze two more things: First we take a look at how the number
of training and testing samples influences the identification accuracy.
This is especially important in an application where the user can't be
required to sit in front of a monitor for one hour just to log in.
Second we take a look whether a weekly tasks independent setting works for gaze
biometrics. That is, the user this time does not have the same stimulus for
training and testing but in both cases multiple different stimuli. For this task we use a
new data set namely the MIT data set by Judd et al. from 2009. here 39 users looked
at photos for 3 seconds each. Overall each user looked at the photos for 50
minutes. In our results we found that a weakly tasks independent setting works
quite well for gaze biometrics. In this plot you can see how increasing the
number of training images increases the accuracy, when the number of test images
is fixed to 30. 30 images means that the user was recorded for 90 seconds. And
also with 90 seconds of training images we can see that we have an accuracy of
about 85 percent. When we fix the number of training images to 30 and increase
the number of test images, we once again see that with more test images and
therefore more trajectories the accuracy improves. But at only 90 seconds of test
data we already have an accuracy of over 86% and it doesn't increase that much
thereafter. To conclude, in our work we first improved the state-of-the-art task
dependent identification by 5.2 percentage points. Second we found that
task independent identification is not currently possible with the classifiers
we tested. Actually the performance is highly asymmetrical and depends on the
task that was used for training and for testing. Last we analyzed how many
training and testing data is needed for an accurate identification and found
that with only 90 seconds of training and testing data and accuracy of over
86% can be achieved. 94% accuracy can only be achieved when the training data
is very large. All our code is freely available on our homepage.
For future work we would like to decrease the number of training and
testing samples needed even more to make the identification faster. Also the
weighting from the predictions of the saccades and fixations was done by
averaging in our work. There might be a better way to do it and also consider
which part of the trajectory the saccade of fixation belongs to. Last, recurrent
neural networks seem to be a promising method where no feature engineering steps
would be needed. Thank you very much for your attention.