4,409 research outputs found
Open Gaze: Open Source eye tracker for smartphone devices using Deep Learning
Eye tracking has been a pivotal tool in diverse fields such as vision
research, language analysis, and usability assessment. The majority of prior
investigations, however, have concentrated on expansive desktop displays
employing specialized, costly eye tracking hardware that lacks scalability.
Remarkably little insight exists into ocular movement patterns on smartphones,
despite their widespread adoption and significant usage. In this manuscript, we
present an open-source implementation of a smartphone-based gaze tracker that
emulates the methodology proposed by a GooglePaper (whose source code remains
proprietary). Our focus is on attaining accuracy comparable to that attained
through the GooglePaper's methodology, without the necessity for supplementary
hardware. Through the integration of machine learning techniques, we unveil an
accurate eye tracking solution that is native to smartphones. Our approach
demonstrates precision akin to the state-of-the-art mobile eye trackers, which
are characterized by a cost that is two orders of magnitude higher. Leveraging
the vast MIT GazeCapture dataset, which is available through registration on
the dataset's website, we successfully replicate crucial findings from previous
studies concerning ocular motion behavior in oculomotor tasks and saliency
analyses during natural image observation. Furthermore, we emphasize the
applicability of smartphone-based gaze tracking in discerning reading
comprehension challenges. Our findings exhibit the inherent potential to
amplify eye movement research by significant proportions, accommodating
participation from thousands of subjects with explicit consent. This
scalability not only fosters advancements in vision research, but also extends
its benefits to domains such as accessibility enhancement and healthcare
applications.Comment: 26 pages , 15 figure
Learning to Personalize in Appearance-Based Gaze Tracking
Personal variations severely limit the performance of appearance-based gaze
tracking. Adapting to these variations using standard neural network model
adaptation methods is difficult. The problems range from overfitting, due to
small amounts of training data, to underfitting, due to restrictive model
architectures. We tackle these problems by introducing the SPatial Adaptive
GaZe Estimator (SPAZE). By modeling personal variations as a low-dimensional
latent parameter space, SPAZE provides just enough adaptability to capture the
range of personal variations without being prone to overfitting. Calibrating
SPAZE for a new person reduces to solving a small optimization problem. SPAZE
achieves an error of 2.70 degrees with 9 calibration samples on MPIIGaze,
improving on the state-of-the-art by 14 %. We contribute to gaze tracking
research by empirically showing that personal variations are well-modeled as a
3-dimensional latent parameter space for each eye. We show that this
low-dimensionality is expected by examining model-based approaches to gaze
tracking. We also show that accurate head pose-free gaze tracking is possible
Unobtrusive and pervasive video-based eye-gaze tracking
Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe
DynamicRead: Exploring Robust Gaze Interaction Methods for Reading on Handheld Mobile Devices under Dynamic Conditions
Enabling gaze interaction in real-time on handheld mobile devices has
attracted significant attention in recent years. An increasing number of
research projects have focused on sophisticated appearance-based deep learning
models to enhance the precision of gaze estimation on smartphones. This
inspires important research questions, including how the gaze can be used in a
real-time application, and what type of gaze interaction methods are preferable
under dynamic conditions in terms of both user acceptance and delivering
reliable performance. To address these questions, we design four types of gaze
scrolling techniques: three explicit technique based on Gaze Gesture, Dwell
time, and Pursuit; and one implicit technique based on reading speed to support
touch-free, page-scrolling on a reading application. We conduct a
20-participant user study under both sitting and walking settings and our
results reveal that Gaze Gesture and Dwell time-based interfaces are more
robust while walking and Gaze Gesture has achieved consistently good scores on
usability while not causing high cognitive workload.Comment: Accepted by ETRA 2023 as Full paper, and as journal paper in
Proceedings of the ACM on Human-Computer Interactio
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