Precise localization of eye centers in low resolution color images

Skodras, Evangelos; Fakotakis, Nikos

doi:10.1016/j.imavis.2015.01.006

Cited by 43 publications

(20 citation statements)

References 28 publications

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“…The proposed approach uses the fast radial symmetry transform as the basis for detecting the pupil center. The FRST was also used in the algorithm presented by Skodras et al [15] for remote eye tracking systems. Our contribution was directed at validating the use of the symmetry transform in head-mounted, gray-scale, high-resolution images.…”

Section: Methodsmentioning

confidence: 99%

Fast and robust ellipse detection algorithm for head-mounted eye tracking systems

Martinikorena

Cabeza

Villanueva

et al. 2018

Machine Vision and Applications

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In head-mounted eye tracking systems, the correct detection of pupil position is a key factor in estimating gaze direction. However, this is a challenging issue when the videos are recorded in real-world conditions, due to the many sources of noise and artifacts that exist in these scenarios, such as rapid changes in illumination, reflections, occlusions and an elliptical appearance of the pupil. Thus, it is an indispensable prerequisite that a pupil detection algorithm is robust in these challenging conditions. In this work, we present one pupil center detection method based on searching the maximum contribution point to the radial symmetry of the image. Additionally, two different center refinement steps were incorporated with the aim of adapting the algorithm to images with highly elliptical pupil appearances. The performance of the proposed algorithm is evaluated using a dataset consisting of 225,569 head-mounted annotated eye images from publicly available sources. The results are compared with the better algorithm found in the bibliography, with our algorithm being shown as superior.

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Section: Methodsmentioning

confidence: 99%

Fast and robust ellipse detection algorithm for head-mounted eye tracking systems

Martinikorena

Cabeza

Villanueva

et al. 2018

Machine Vision and Applications

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“…In recent years, several gaze and head tracking methods have been proposed [30], [31]. As one of the most popular open-source facial analysis tools, OpenFace is utilized for the purpose of extracting the features of the driver's gaze and head due to its fine performance and robustness.…”

Section: Feature Extractionmentioning

confidence: 99%

A Dual-Cameras-Based Driver Gaze Mapping System With an Application on Non-Driving Activities Monitoring

Yang

Dong

Dmitruk

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Characterisation of the driver's non-driving activities (NDAs) is of great importance to the design of the takeover control strategy in Level 3 automation. Gaze estimation is a typical approach to monitor the driver's behaviour since the eye gaze is normally engaged with the human activities. However, current eye gaze tracking techniques are either costly or intrusive which limits their applicability in vehicles. This paper proposes a low-cost and non-intrusive dual-cameras based gaze mapping system that visualises the driver's gaze using a heat map. The challenges introduced by complex head movement during NDAs and camera distortion are addressed by proposing a nonlinear polynomial model to establish the relationship between the face features and eye gaze on the simulated driver's view. The Root Mean Square Error of this system in the in-vehicle experiment for the X and Y direction is 7.80±5.99 pixel and 4.64±3.47 pixel respectively with the image resolution of 1440 × 1080 pixels. This system is successfully demonstrated to evaluate three NDAs with visual attention. This technique, acting as a generic tool to monitor driver's visual attention, will have wide applications on NDA characterisation for intelligent design of take over strategy and driving environment awareness for current and future automated vehicles.

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“…To this end, an algorithm for accurate eye center localization in low resolution images based on our previous work [46,47] is used. In order to robustly and accurately detect eye centers, the system builds upon a synergy of chrominance information and radial symmetry.…”

Section: Detection Of Eye Centersmentioning

confidence: 99%