Robust Pupil Localization Algorithm under Off-axial Pupil Occlusion

Dewi, Desak Ayu Sista; Wibirama, Sunu; Ardiyanto, Igi

doi:10.1109/biomic48413.2019.9034855

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…However, in low-quality eye images, due to the Hough transform circle detection method, there may be multiple round areas similar to pupils in the image, increasing pupil positioning error. In order to solve the problem that pupil localization is prone to failure in the case of offaxis occlusion, Dewi et al [13] proposed an ellipse-fitting and a fine-adjustment algorithm for robust pupil localization in off-axis conditions. The accuracy of this method was 0.83 when the Z-value was 41.5.…”

Section: Related Workmentioning

confidence: 99%

A Robust Pupil Localization via a Novel Parameter Optimization Strategy

Zhou

Wang

2022

Wireless Communications and Mobile Computing

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With the development of iris biometrics, the pupil recognition method is widely used in the fields of identity recognition and so on. Most traditional iris recognition algorithms use an adaptive threshold to the eye image binarization processing; although this can at the maximum preserve the original characteristics of the image itself, the retained features in this method included a lot of noise points, including Gaussian noise and so on, in a series of image preprocessing. The subsequent iris recognition and pupil positioning are still prone to the loss of positioning accuracy, which cannot separate the pupil completely from the background area of the image, increasing the difficulty of connecting the pupil to domain localization, thus affecting the pupil positioning accuracy. Therefore, in order to meet the requirement of high precision pupil recognition accuracy in low-quality eye images, this paper takes the iris dataset provided by the China Research Institute of Automation as an example to improve the traditional pupil location method. A pupil localization method based on parameter optimization is proposed, which includes the setting of a three-step threshold, called as TST.

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Section: Related Workmentioning

confidence: 99%

A Robust Pupil Localization via a Novel Parameter Optimization Strategy

Zhou

Wang

2022

Wireless Communications and Mobile Computing

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“…Despite the high-accuracy gaze estimation achieved just after the gaze-mapping calibration, interpolation-based methods usually decrease their accuracy because they are susceptible to various factors, such as low-resolution eye images [ 2 , 3 ], natural head movements [ 4 , 5 ], poor gaze-mapping calibration [ 6 , 7 ], eye occlusions [ 8 , 9 ], the geometry of eye tracker components [ 10 , 11 ], nonlinearity of eye feature distribution [ 4 , 12 ], among others [ 1 , 13 ]. The eye-camera location has an essential role in the gaze estimation accuracy in both RET and HMET because the location defines the perspective and distribution of the eye feature on the eye image plane.…”

Section: Introductionmentioning

confidence: 99%

High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods

Narcizo

Santos

Hansen

2021

Vision

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This study investigates the influence of the eye-camera location associated with the accuracy and precision of interpolation-based eye-tracking methods. Several factors can negatively influence gaze estimation methods when building a commercial or off-the-shelf eye tracker device, including the eye-camera location in uncalibrated setups. Our experiments show that the eye-camera location combined with the non-coplanarity of the eye plane deforms the eye feature distribution when the eye-camera is far from the eye’s optical axis. This paper proposes geometric transformation methods to reshape the eye feature distribution based on the virtual alignment of the eye-camera in the center of the eye’s optical axis. The data analysis uses eye-tracking data from a simulated environment and an experiment with 83 volunteer participants (55 males and 28 females). We evaluate the improvements achieved with the proposed methods using Gaussian analysis, which defines a range for high-accuracy gaze estimation between −0.5∘ and 0.5∘. Compared to traditional polynomial-based and homography-based gaze estimation methods, the proposed methods increase the number of gaze estimations in the high-accuracy range.

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Robust Pupil Localization Algorithm under Off-axial Pupil Occlusion

Cited by 2 publications

References 10 publications

A Robust Pupil Localization via a Novel Parameter Optimization Strategy

A Robust Pupil Localization via a Novel Parameter Optimization Strategy

High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods

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