Real Time Eyeball Movement Detection Based on Region Division and Midpoint Position

Prasetya, Renaldi Primaswara; Utaminingrum, Fitri; Mahmudy, Wayan Firdaus

doi:10.22266/ijies2018.0630.16

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Our proposed method also compared with another previous method like Region Division (RD) method [17], Triangle Similarity (TS) [15], Naïve Bayes Triangle Similarity (NBTS) [16], and Coordinate Geometry (CG) method [18].…”

Section: Results and Analysismentioning

confidence: 99%

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Electric Wheelchair Control Mechanism Using Eye-mark Key Point Detection

Pangestu¹,

Utaminingrum²

2020

IJIES

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People with deficiency hands and feet, especially people with multiple disabilities has difficulty for navigation. Therefore, this research proposed an approach to help them using an electric wheelchair and control it using their eyeball movements. However, eyeball movement detection is still a problem due to the lack of standardized methods such as low accuracy for several gazes especially for downward and forward movements that caused by failure tracking mechanisms. Failure tracking mechanism mostly caused by failure pupil detection and tracking processes. According to the conducted research, there is a relation between eyeball movement and eyelid movement. Due to the relation, it can be concluded that the movement of the eyeball can be detected by utilize the eyelid movement without using the tracking mechanism. Hence, in this research, we propose an approach to detect the five movements of the eyeball using Eye-mark based on contour and edge detection. Our proposed method produces a brisk result with 91,2% of accuracy and better computational time compared with other marking methods. Accordingly, it is interesting on how to relate between eyeball movements and electric wheelchair navigation mechanism.

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Section: Results and Analysismentioning

confidence: 99%

“…Several research such as using Region Division [17] and Geometry Approach [18] also proposed 5 gaze eyeball movement detection. However, those methods still lack in the forward movement detection accuracies.…”

Section: Previous Workmentioning

confidence: 99%

Electric Wheelchair Control Mechanism Using Eye-mark Key Point Detection

Pangestu¹,

Utaminingrum²

2020

IJIES

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“…They did not work on eye gaze tracking and did not get a success rate in the case of downward eyeball detection, where the percentage of detection success reached 79%. RP Prasetya et al [15] proposed a method for the eye region box (both the vertical and horizontal division) using the Haar Cascade method and the KCF filter tracker with a low sensitivity level, where the computational time was low. Utaminingrum et al [16] proposed a method for detecting the area of both eyes and the Hough Circle Transform of an eye gaze position.…”

Section: Literature Reviewmentioning

confidence: 99%

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Chakraborty¹,

Ahmed²,

Yousuf³

et al. 2021

IEEE Access

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Attention is the mental awareness of human on a particular object or a piece of information. The level of attention indicates how intense the focus is on an object or an instance. In this study, several types of human attention level have been observed. After introducing image segmentation and detection technique for facial features, eyeball movement and gaze estimation were measured. Eye movement were assessed using the video data, and a total of 10197 data instances were manually labelled for the attention level. Then Artificial Neural Network (ANN) and Recurrent Neural Network-Long Short Term Memory (LSTM) based Deep learning (DL) architectures have been proposed for analysing the data. Next, the trained DL model has been implanted into a robotic system that is capable of detecting various features; ultimately leading to the calculation of visual attention for reading, browsing, and writing purposes. This system is capable of checking the attention level of the participants and also can detect if participants are present or not. Based on a certain level of visual focus of attention (VFOA), this system interacts with the person, generates awareness and establishes verbal or visual communication with that person. The proposed ML techniques have achieved almost 99.24% validation accuracy and 99.43% test accuracy. It is also shown in the comparative study that, since the dataset volumes are limited, ANN is more suitable for attention level calculation than RNN-LSTM. We hope that the implemented robotic structure manifests the real-world implication of the proposed method.

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“…Another strategy utilizing the Triangle similitude to distinguish the eyeball development, yet just can identify four look course, including right, left, up and descending and furthermore have awful exactness for recognizing the descending development about just 58% [10]. Other research also has not good result for detecting the downward direction by using midpoint and Region Division, and also this method just can detect only four gaze direction and need a registration process too [11].…”

Section: Introductionmentioning

confidence: 99%

Eyeball Movement Detection To Control Smart Wheelchair Using Eye Aspect Ratio (EAR)

Pangestu

2021

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Many developed technology's with an aim of helping the disabilities. One of them is a wheelchair. It is the most common stuff that used for helping disabilities as a tool for mobilization. There are two types of wheelchair. The first is the manual wheelchair, operated by hand. The second is an electrical wheelchair, that operated by joystick or other electric device. This research proposed a mechanism to control the wheelchair by using an eye movement. It could be used especially for people with multiple disabilities (hand and foot defects), so they can take an advantage of their eyeballs as a tool to control wheelchair movement. There are five options for controlling the wheelchair (leftward, rightward, upward, downward and center). Leftward, rightward and center used for control direction of smart wheelchair. Furthermore, upward and downward of eye movements used to control the speed of smart wheelchair. Upward command used to increase the speed. Meanwhile, down-ward used to decrease the speed (stop). The proposed method used EAR (Eye Aspect Ratio), which divided into three regions based on sector area, for determining the directions of the eyeball movement. EAR is the value that represents the ratio between the upper eyelid and lower eyelid. The result obtained high accuracy

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Real Time Eyeball Movement Detection Based on Region Division and Midpoint Position

Cited by 5 publications

References 21 publications

Electric Wheelchair Control Mechanism Using Eye-mark Key Point Detection

Electric Wheelchair Control Mechanism Using Eye-mark Key Point Detection

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Eyeball Movement Detection To Control Smart Wheelchair Using Eye Aspect Ratio (EAR)

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