LIMBUSTRACK: STABLE EYE-TRACKING IN IMPERFECT LIGHT CONDITIONS

We are aware of only one serious effort at development of a cheap, accurate, wearable eye tracker: the open source openEyes project. However, its method of ocular feature detection is such that it is prone to failure in variable lighting conditions. To address this deficiency, we have developed a cheap wearable eye tracker. At the heart of our development are novel techniques that allow operation under variable illumination

Eye-tracking assistive technologies for individuals with amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis, also known as ALS, is a progressive nervous system disorder that affects nerve cells in the brain and spinal cord, resulting in the loss of muscle control. For individuals with ALS, where mobility is limited to the movement of the eyes, the use of eye-tracking-based applications can be applied to achieve some basic tasks with certain digital interfaces. This paper presents a review of existing eye-tracking software and hardware through which eye-tracking their application is sketched as an assistive technology to cope with ALS. Eye-tracking also provides a suitable alternative as control of game elements. Furthermore, artificial intelligence has been utilized to improve eye-tracking technology with significant improvement in calibration and accuracy. Gaps in literature are highlighted in the study to offer a direction for future research

A Novel approach to a wearable eye tracker using region-based gaze estimation

Eye tracking studies are useful to understand human behavior and reactions to visual stimuli. To conduct experiments in natural environments it is common to use mobile or wearable eye trackers. To ensure these systems do not interfere with the natural behavior of the subject during the experiment, they should be comfortable and be able to collect information about the subject\u27s point of gaze for long periods of time. Most existing mobile eye trackers are costly and complex. Furthermore they partially obstruct the visual field of the subject by placing the eye camera directly in front of the eye. These systems are not suitable for natural outdoor environments due to external ambient light interfering with the infrared illumination used to facilitate gaze estimation. To address these limitations a new eye tracking system was developed and analyzed. The new system was designed to be light and unobtrusive. It has two high definition cameras mounted onto headgear worn by the subject and two mirrors placed outside the visual field of the subject to capture eye images. Based on the angular perspective of the eye, a novel gaze estimation algorithm was designed and optimized to estimate the gaze of the subject in one of nine possible directions. Several methods were developed to compromise between shape-based models and appearance-based models. The eye model and features were chosen based on the correlation with the different gaze directions. The performance of this eye tracking system was then experimentally evaluated based on the accuracy of gaze estimation and the weight of the system

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