In my poster, I'm presenting the eye tracking data
of a combined eyetracking and EEG study using natural, unconstrained and life-sized
stimuli in a free viewing paradigm.
We find the face-attraction bias meaning that our participants tend to look
at human faces first.
We investigate this effect with a mixed linear model to see which factors influence it.
And finally, we discuss the implications for ERP research.
The blink-induced displacements observed in electrooculography signals are mainly due
to eyelid movement. Specifically, the eyelids behave like sliding
electrodes which increasingly shunt the positively-charged
corneas to the electrodes as they slide over the eyeball surface.
This work augments an already-published battery model of the eye to model this effect,
thereby creating a more generalised model of the EOG potential.
We show that this model accurately represents the blink-induced EOG signal.
Accurate eye image segmentation is crucial to improve gaze estimation.
However to train these eye image segmentation networks we require a large annotated data set.
We propose to leverage, large number of easily available unlabeled images, by guessing
the labels using two proposed frameworks using domain specific augmentation and novel
spatially varying image transformations. With this we can improve classification
performance for all best cases and accuracy with 0.1% even when using 1% of the data set.
Deep appearance-based gaze estimation methods mainly rely on static features.
However, gaze is a dynamic process.
So, in this paper, we investigate whether temporal information can
improve gaze estimation accuracy in VR scenarios,
and how beneficial this information can be when dealing with specific eye movements.
To do so, we build several spatio-temporal models based on
a combination of convolutional and recurrent neural networks
with a dataset of eye-image sequences captured at 100Hz.
For more information on the experimental evaluation and results,
please drop by our poster or check out our paper.
My name is brendan and I'll be presenting our work
on predicting the intent to interact in virtual reality.
The intent to interact is the when of interaction
and it can be modeled using gaze dynamics
for prediction. We predict the moment a user clicks to
select an item in a virtual reality environment.
We evaluated the use of logistic regression models to predict the intent to interact
and present results on feature selection and model performance.
Please come see us during the poster session to find out more.
55 rides is a database of gaze behavior during naturalistic driving
It contains 55 rides during day and nighttime, with different weather,
subjects and traffic conditions Gaze was extracted with an appearance based
deep neural network approach and multiple raters annotated driver attention continuously
We also provide a baseline comparsion for prediction of driver attention based on head
and eye movement In this paper, we investigated the performance
of the pinch gesture as a selection technique for gaze-based systems.
To do this, we used a Fitts’ law task where participants selected the targets either by
pinch, clicking a button, or dwell. Results showed that,
dwell had the fewest errors, button clicks achieved the best performance
yet, pinch and button clicks were not significantly
different in all measures, making pinch an attractive option for the future.
eye privacy has been an area of interest for the eye tracking community, as it contains
sensitive information. So we propose changing the video capture
pipeline by modifying the iris texture in the eye images with synthetic version.
For this, given any eye image and its ellipse fits, we model the fake iris based on
real iris. The fake iriss undergoes, a rubber sheet model transformation followed
by numerous other transformation taking into account information from real eye image to
get the final image. We show with this approach, there is no significant difference
in eye tracking performance.
How do eye movements change when we show the same figures again
after longer time-spans? This is what we looked at in our research.
We tested the effect of showing the same figure again after
a week a month and six months using examples
from risk reports. We found out that there
are some differences regarding duration, fixation and accuracy
there was no clear pattern. It might be worth researching this in the future.
Hi everybody, I am Lorenzo Diana, a PhD student in Clinical Neuroscience at the University
of Milano-Bicocca. I am very glad to present our work entitled
Saccades, attentional orienting and disengagement:
the effects of anodal tDCS over right posterior parietal cortex and frontal eye field
By increasing the excitability of the right parietal and frontal corteces
of healthy participants we observed faster saccadic reaction times
Fixation is a free and open-source framework for experimental eye movement research,
agnostic to the source of the raw gaze stream and can be used with any eye
tracking platform, by mapping data to a JSON format
and streaming it in real time. A researcher can record and visualize gaze
data and export it for analysis using an offline
web application, without proprietary components or licensing restrictions.
We leverage web technologies to address data privacy concerns,
and demonstrate support for high-frequency recording and real-time visualization.
Visual attention in the peripheral field of vision is evaluated
using estimated microsaccade frequency from both eyes with the Bayesian modelling
technique.