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Article

Focus Assessment Method of Gaze Tracking Camera Based on ε-Support Vector Regression

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea
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Academic Editor: Hari M. Srivastava
Symmetry 2017, 9(6), 86; https://doi.org/10.3390/sym9060086
Received: 16 April 2017 / Revised: 6 June 2017 / Accepted: 8 June 2017 / Published: 14 June 2017
In order to capture an eye image of high quality in a gaze-tracking camera, an auto-focusing mechanism is used, which requires accurate focus assessment. Although there has been previous research on focus assessment in the spatial or wavelet domains, there are few previous studies that combine all of the methods of spatial and wavelet domains. Since all of the previous focus assessments in the spatial or wavelet domain methods have disadvantages, such as being affected by illumination variation, etc., we propose a new focus assessment method by combining the spatial and wavelet domain methods for the gaze-tracking camera. This research is novel in the following three ways, in comparison with the previous methods. First, the proposed focus assessment method combines the advantages of spatial and wavelet domain methods by using ε-support vector regression (SVR) with a symmetrical Gaussian radial basis function (RBF) kernel. In order to prevent the focus score from being affected by a change in image brightness, both linear and nonlinear normalizations are adopted in the focus score calculation. Second, based on the camera optics, we mathematically prove the reason for the increase in the focus score in the case of daytime images or a brighter illuminator compared to nighttime images or a darker illuminator. Third, we propose a new criterion to compare the accuracies of the focus measurement methods. This criterion is based on the ratio of relative overlapping amount (standard deviation of focus score) between two adjacent positions along the Z-axis to the entire range of focus score variety between these two points. Experimental results showed that the proposed method outperforms other methods. View Full-Text
Keywords: gaze-tracking camera; auto-focusing; focus assessment; ε-support vector regression with a symmetrical Gaussian radial basis function kernel; camera optics gaze-tracking camera; auto-focusing; focus assessment; ε-support vector regression with a symmetrical Gaussian radial basis function kernel; camera optics
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MDPI and ACS Style

Luong, D.T.; Kang, J.S.; Nguyen, P.H.; Lee, M.B.; Park, K.R. Focus Assessment Method of Gaze Tracking Camera Based on ε-Support Vector Regression. Symmetry 2017, 9, 86. https://doi.org/10.3390/sym9060086

AMA Style

Luong DT, Kang JS, Nguyen PH, Lee MB, Park KR. Focus Assessment Method of Gaze Tracking Camera Based on ε-Support Vector Regression. Symmetry. 2017; 9(6):86. https://doi.org/10.3390/sym9060086

Chicago/Turabian Style

Luong, Duc T., Jeon S. Kang, Phong H. Nguyen, Min B. Lee, and Kang R. Park 2017. "Focus Assessment Method of Gaze Tracking Camera Based on ε-Support Vector Regression" Symmetry 9, no. 6: 86. https://doi.org/10.3390/sym9060086

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