Eye gaze tracking based directional control interface for interactive applications

Laddi, Amit; Prakash, Neelam Rup

doi:10.1007/s11042-019-07940-3

Cited by 22 publications

(7 citation statements)

References 21 publications

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“…Compared with current advanced methods on the BioID database, as shown in Table 9. Table 9 Comparison of localization accuracy on the BioID database Method e≤0.05 e≤0.1 e≤0.25 Timm2011 [18] 82.5% 93.4% 98.0% Valenti2008 [19] 84.1% 90.9% 98.5% Valenti2012 [20] 86.1% 91.7% 97.9% Markuš2014 [12] 89.9% 97.1% 99.7% Ren2014 [15] 77.1% 92.3% 99.0% Cai2015 [4] 84.1% 95.6% 99.8% Pang2015 [14] 83.6% 96.1% 98.6% Zhang2016 [24] 85.7% 93.7% 99.2% Araujo2017 [3] 88.3% 92.7% 98.9% Xia2018 [23] 87.1% 98.7% 99.9% Ahmed 2019 [1] 87.0% 95.2% 99.0% Laddi2019 [10] 81.4% 92.2% 97.5% Proposed method 90.1% 98.8%…”

Section: Localization Resultsmentioning

confidence: 99%

“…However, Gaussian function is mainly composed of an exponent which ranges from 0 to 1, while the gray value of an image ranges from 0 to 255. Therefore, the two-dimensional Gaussian function with the peak value 255 in the eye ROI midpoint, can be selected as the corresponding energy function, which can be modeled as [5] , (10) where (xm, ym) is the two-dimensional coordinate of the eye ROI midpoint, σ is the energy decay radius in the energy map. According to (10), the energy maps based on the eye ROI midpoint can be generated, shown in the fourth column in Table 1.…”

Section: Energy Map Based On the Eye Roi Midpointmentioning

confidence: 99%

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Iris center localization using energy map synthesis based on gradient and isophote

Dai

Liu

et al. 2020

IFS

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Gaze tracking has wide applications such as in driver fatigue detection, virtual reality, and human-computer interaction. The performance of gaze tracking depends largely on the accuracy of iris center localization. However, most of the existing gaze tracking products are intrusive or require additional equipment with a high cost. Therefore, precise localization methods of iris center in low quality images captured in a non-contact way with visible light need to be investigated. This paper proposes a novel localization method of iris center using energy map synthesis based on image gradient, isophote and midpoint of eye ROI (Region of interest). This method combines the advantages of higher localization accuracy based on gradient, invariance to the rotation and linear transformation of light based on isophote, and iris center close to the midpoint of eye ROI. Moreover, a post-processing correction method for the closed eyes and for other large deviations of iris center position is adopted to further improve the localization accuracy. The algorithm is verified on the BioID, Talking Face Video and MUCT Face databases, and the results show that the localization accuracy has outperformed the listed state-of-the-art methods in varying illuminations.

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Section: Localization Resultsmentioning

confidence: 99%

Section: Energy Map Based On the Eye Roi Midpointmentioning

confidence: 99%

Iris center localization using energy map synthesis based on gradient and isophote

Dai

Liu

et al. 2020

IFS

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“…They adopt a SIFT descriptor and the k-nearest neighbor algorithm to achieve an accurate eye center location, however leading to a high computationally cost. Moreover, Laddi et al integrate the supervised and unsupervised algorithms, wherein the supervised regression method is used to extract eye regions and the unsupervised gradient method is adopted to localize iris center [13]. By comparing the above methods, in order to meet the requirements of real-time and accurate localization in applications, based on the gradient method proposed by Timm and Barth [10], an iris center localization method using the image inpaint technology, energy map synthesis and post-processing correction is proposed in the paper.…”

Section: B Iris Center Localization Methodsmentioning

confidence: 99%

Iris Center Localization Using Energy Map With Image Inpaint Technology and Post-Processing Correction

Dai

Liu

et al. 2020

IEEE Access

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Iris center localization is the basis of iris biometrics, face recognition and gaze tracking. However, individual differences, changes in facial expression, varying light conditions, occlusion, and so on, all bring great challenges to accurately localize the iris center. In order to improve localization accuracy in low-quality images and meet the need of efficiency in practical applications, a novel method of iris center localization is proposed in this paper using energy map synthesis based on image gradient, image inpaint technology, and post-processing correction. The image inpaint technology is firstly adopted to inhibit the effect of some specular reflection. Then the energy maps based on image gradient and eye ROI (Region Of Interest) midpoint are synthesized to significantly improve the localization accuracy. In the end, post-processing correction is carried out to eliminate influence of the closed eye and other large derivations to further improve the localization accuracy. The algorithm is verified on the challenging BioID database, Talking Face Video database and the MUCT face database. The result shows the localization accuracy has outperformed the state-of-the-art unsupervised methods on the three databases, and it is suitable for real-time applications. INDEX TERMS Iris center localization, image gradient, image inpaint, energy map synthesis, post-processing correction.

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“…Bifocal, trifocal, and progressive spectacles can benefit vision for multiple distances without changing phenomena. Although these traditional optical glasses that passively adapt to the needs of the human eye can solve the problem of near-sight blurring in presbyopia, they still have some adverse effects, such as discomfort and inability to adapt well to different gaze distances, blurred vision, and dizziness 1 . Pixel Optics company has proposed a method to control electrical current to change the refractive properties of liquid crystals through head posture detection.…”

Section: Introductionmentioning

confidence: 99%

Gaze distance detection by binocular tracking for automatic zoom glasses

et al. 2023

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.To realize automatic zooming of presbyopia glasses, we proposed a gaze distance detection method based on binocular tracking. A theoretical model was built to calculate gaze distance based on the gaze directions of two eyeballs. The gaze direction of each eyeball was obtained by the pupil center corneal reflection method. Three light-emitting diodes (LEDs) and two miniature cameras were placed on the glasses frame around the eyeball. Then, gaze direction was derived by calculating the distances between the pupil center and the positions of three LED reflection images by the corneal from the images captured by cameras. The experiment setup was built to test the gaze direction and gaze distance. The experimental result demonstrates that it is an effective way to obtain the user’s gaze distance for automatically adjusting the focal length of the zoom glasses.

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Eye gaze tracking based directional control interface for interactive applications

Cited by 22 publications

References 21 publications

Iris center localization using energy map synthesis based on gradient and isophote

Iris center localization using energy map synthesis based on gradient and isophote

Iris Center Localization Using Energy Map With Image Inpaint Technology and Post-Processing Correction

Gaze distance detection by binocular tracking for automatic zoom glasses

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