Comparison of two techniques for gaze estimation system using the direction of eyes and head

Sakurai, Kouhei; Yan, Mingmin; Tamura, H.; Tanno, Koichi

doi:10.1109/smc.2016.7844609

Cited by 2 publications

(1 citation statement)

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“…RGB-D sensor can augment the conventional image with depth information (related with the distance to the sensor) in a per-pixel basis. In addition, we proposed a system that combined RGB-D sensor and an electrooculogram (EOG) sensor to estimate the gaze [11,12]. However, this proposed system did not have high enough accuracy to use for car driving evaluation (average error of eyeball angle is over 10 degrees).…”

Section: Introductionmentioning

confidence: 99%

A Study on the Gaze Range Calculation Method During an Actual Car Driving Using Eyeball Angle and Head Angle Information

Sakurai

Tamura

2019

Sensors

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Car operation requires advanced brain function. Currently, evaluation of the motor vehicle driving ability of people with higher brain dysfunction is medically unknown and there are few evaluation criteria. The increase in accidents by elderly drivers is a social problem in Japan, and a method to evaluate whether elderly people can drive a car is needed. Under these circumstances, a system to evaluate brain dysfunction and driving ability of elderly people is needed. Gaze estimation research is a rapidly developing field. In this paper, we propose the gaze calculation method by eye and head angles. We used the eye tracking device (TalkEyeLite) made by Takei Scientific Instruments Cooperation. For our image processing technique, we estimated the head angle using the template matching method. By using the eye tracking device and the head angle estimate, we built a system that can be used during actual on-road car operation. In order to evaluate our proposed method, we tested the system on Japanese drivers during on-road driving evaluations at a driving school. The subjects were one instructor of the car driving school and eight general drivers (three 40–50 years old and five people over 60 years old). We compared the gaze range of the eight general subjects and the instructor. As a result, we confirmed that one male in his 40s and one elderly driver had narrower gaze ranges.

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Section: Introductionmentioning

confidence: 99%

A Study on the Gaze Range Calculation Method During an Actual Car Driving Using Eyeball Angle and Head Angle Information

Sakurai

Tamura

2019

Sensors

Self Cite

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An AI-Based Online Exam Proctoring Framework

Jain¹,

Gupta²,

Sahil³

et al. 2022

JCOM

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In recent years, there has been an increase in online education. However, an adequate technique was not developed for online examinations. Some educational facilities have compiled assignments that students may copy and paste from the web, while others use remote proctoring, where a human proctor monitors what the students are doing online. Cheating in online tests remains common despite all the growth that has taken place in this field. This paper proposes an Artificial Intelligence (AI)-based system that can assist in the automatic detection of fraud in online tests. This method is both efficient and trustworthy. This system outperformed previous systems in experiments.

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Comparison of two techniques for gaze estimation system using the direction of eyes and head

Cited by 2 publications

References 8 publications

A Study on the Gaze Range Calculation Method During an Actual Car Driving Using Eyeball Angle and Head Angle Information

A Study on the Gaze Range Calculation Method During an Actual Car Driving Using Eyeball Angle and Head Angle Information

An AI-Based Online Exam Proctoring Framework

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