Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision

Khan, Wasiq; Hussain, Abir Jaafar; Kuru, Kaya; Alaskar, Haya

doi:10.3390/s20133785

Cited by 34 publications

(23 citation statements)

References 38 publications

(88 reference statements)

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“…In order to evaluate the performance of the proposed method, the three traditional methods [ 6 , 8 ] were implemented for comparisons. Table 5 shows the comparison and analysis between the proposed method and the three traditional methods [ 6 , 8 ], and p -values using paired t-test between the three traditional methods and the proposed method. More specifically, the paired t-test was used to estimate whether the differences between the performance of three traditional methods and that of the proposed method were significant or not.…”

Section: Resultsmentioning

confidence: 99%

Section: Comparisons With the State-of-the-art Approachesmentioning

confidence: 99%

“…In recent years, video-oculography has been used increasingly because of its advantages of non-invasiveness [ 4 , 5 , 6 , 7 ]. With the vigorous development of machine learning, more and more applications are also appearing in eye science [ 8 ]. The current commercially available high-resolution, research-grade eye trackers are often very expensive.…”

Section: Introductionmentioning

confidence: 99%

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A Fast and Effective System for Analysis of Optokinetic Waveforms with a Low-Cost Eye Tracking Device

2020

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Optokinetic nystagmus (OKN) is an involuntary eye movement induced by motion of a large proportion of the visual field. It consists of a “slow phase (SP)” with eye movements in the same direction as the movement of the pattern and a “fast phase (FP)” with saccadic eye movements in the opposite direction. Study of OKN can reveal valuable information in ophthalmology, neurology and psychology. However, the current commercially available high-resolution and research-grade eye tracker is usually expensive. Methods & Results: We developed a novel fast and effective system combined with a low-cost eye tracking device to accurately quantitatively measure OKN eye movement. Conclusions: The experimental results indicate that the proposed method achieves fast and promising results in comparisons with several traditional approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Comparisons With the State-of-the-art Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fast and Effective System for Analysis of Optokinetic Waveforms with a Low-Cost Eye Tracking Device

2020

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“…In addition, there is a study to find the pupil center point on the entire face for gaze tracking. However, since an RGB camera is used and minute changes in pupil size cannot be measured, a new pupil region detection method is required [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

Experimental Verification of Objective Visual Fatigue Measurement Based on Accurate Pupil Detection of Infrared Eye Image and Multi-Feature Analysis

Kim

Lee

2020

Sensors

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As the use of electronic displays increases rapidly, visual fatigue problems are also increasing. The subjective evaluation methods used for visual fatigue measurement have individual difference problems, while objective methods based on bio-signal measurement have problems regarding motion artifacts. Conventional eye image analysis-based visual fatigue measurement methods do not accurately characterize the complex changes in the appearance of the eye. To solve this problem, in this paper, an objective visual fatigue measurement method based on infrared eye image analysis is proposed. For accurate pupil detection, a convolutional neural network-based semantic segmentation method was used. Three features are calculated based on the pupil detection results: (1) pupil accommodation speed, (2) blink frequency, and (3) eye-closed duration. In order to verify the calculated features, differences in fatigue caused by changes in content color components such as gamma, color temperature, and brightness were compared with a reference video. The pupil detection accuracy was confirmed to be 96.63% based on the mean intersection over union. In addition, it was confirmed that all three features showed significant differences from the reference group; thus, it was verified that the proposed analysis method can be used for the objective measurement of visual fatigue.

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“…Then, static feature points are inputted into the SLAM framework to obtain the position and posture of the system. Khan et al [28] proposed a coarse-to-fine pupil localization method using a composite of machine learning and image processing algorithms. The machine learning method they developed can be applied to dynamic SLAM.…”

Section: Introductionmentioning

confidence: 99%

Monocular Visual-Inertial Navigation for Dynamic Environment

Xia

Qiao

2021

Remote Sensing

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Simultaneous localization and mapping (SLAM) systems have been generally limited to static environments. Moving objects considerably reduce the location accuracy of SLAM systems, rendering them unsuitable for several applications. Using a combined vision camera and inertial measurement unit (IMU) to separate moving and static objects in dynamic scenes, we improve the location accuracy and adaptability of SLAM systems in these scenes. We develop a moving object-matched feature points elimination algorithm that uses IMU data to eliminate matches on moving objects but retains them on stationary objects. Moreover, we develop a second algorithm to validate the IMU data to avoid erroneous data from influencing image feature points matching. We test the new algorithms with public datasets and in a real-world experiment. In terms of the root mean square error of the location absolute pose error, the proposed method exhibited higher positioning accuracy for the public datasets than the traditional algorithms. Compared with the closed-loop errors obtained by OKVIS-mono and VINS-mono, those obtained in the practical experiment were lower by 50.17% and 56.91%, respectively. Thus, the proposed method eliminates the matching points on moving objects effectively and achieves feature point matching results that are realistic.

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Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision

Cited by 34 publications

References 38 publications

A Fast and Effective System for Analysis of Optokinetic Waveforms with a Low-Cost Eye Tracking Device

A Fast and Effective System for Analysis of Optokinetic Waveforms with a Low-Cost Eye Tracking Device

Experimental Verification of Objective Visual Fatigue Measurement Based on Accurate Pupil Detection of Infrared Eye Image and Multi-Feature Analysis

Monocular Visual-Inertial Navigation for Dynamic Environment

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