3D GAZE ESTIMATION IN INCONSISTENT CONDITIONS FROM LOW-RESOLUTION IMAGES FOR DESKTOP AND MOBILE DEVICES

Gaze

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tracking

technology

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provides information about the spatial coordinates of a person's point of attention in real-time using the distance to the

camera

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, the position of the head, and the pupil of the

eye

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. The

first

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attempts at

gaze

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tracking began at the end of the 19th century by a professor of psychology and education at Western Pennsylvania University to develop an effective

method

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of the reading process when learning a language. () Nowadays,

gaze

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tracking

technology

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has found application in many fields, including medicine, psychology, marketing, and simulators.().

Although

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ready-made solutions already exist, they have some disadvantages: the need for special equipment (Tobii

Eye

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Tracker), high cost (Tobii Aware), or dependence on external

conditions

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.

This

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research is aimed at creating a

gaze

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tracking

technology

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accessible to everyone who has devices with a reasonable

camera

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.

In other words

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,

this

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technology

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will find application among the audience using computers, laptops, and mobile

phones

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. According to Drewes H. (),

gaze

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tracking already helps motor impaired people interact with a computer, and for ordinary people, it will make it more comfortable,

for example

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, when typing text or as an alternative to a mouse or touchpad for mobile

phones

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. Commercially successful areas,

such

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as the video game market, are

also

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interested in the emergence of a mass

Eye

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tracking

algorithm

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. Veronica S. claims that "Recent innovations in the video game industry include alternative input modalities to provide an enhanced, more immersive user experience.

In particular

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,

eye

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gaze

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control has recently been explored as an input modality in video games." ().

In addition

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to the wide application, computers and mobile

phones

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have several other advantages: high computing power, high-resolution cameras, and their static position on the device, which allows for high-precision calibration ().

However

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, the development of an accurate

Eye

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tracking

algorithm

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requires solving two critical problems: unstable

conditions

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and poor image quality from the front

camera

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. Due to the lack of infrared optical sensors in ordinary cameras, which provides robustness inexpensive devices (Tobii

Eye

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Tracker 5) to changes in shooting environment

conditions

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, the accuracy of

gaze

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estimation will be affected by lighting,

camera

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defocusing, as well as through view and scale changes. (). According to Shreya G., appearance-based and model-based methods are used in the development of

gaze

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-tracking algorithms. (). Appearance-based models

use

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a large amount of data and depend on the

method

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of collecting

this

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data (laboratory or in the wild),

therefore

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they are extremely sensitive to changes in light and noise in the image ().

On the other hand

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, model-based algorithms, according to Anuradha K., "

use

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a geometric model of the human

eye

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to estimate the center of the cornea, optical and visual axes of the

eye

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and estimate the

gaze

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coordinates as points of intersection where the visual axes meet the scene"().

Therefore

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, the model-based

method

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will be used to solve the problem with inconsistent

conditions

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.

However

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, the model-based

method

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is extremely dependent on the

camera

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resolution, which does not allow for the high accuracy of the

algorithm

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. According to Fukuda T. () to reduce the cost of budget devices

such

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as laptops and

phones

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, manufacturers

use

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cheap

camera

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modules whose resolution is too low to

use

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the

technology

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with free head movements and independent face distance from the

camera

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. To solve

this

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problem, the

algorithm

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will

use

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the SRGAN model ().