A Survey of Iris Biometrics Research: 2008–2010

Bowyer, Kevin W.; Hollingsworth, Karen; Flynn, Patrick J.

doi:10.1007/978-1-4471-6784-6_2

Cited by 20 publications

(17 citation statements)

References 205 publications

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“…There are already several techniques for corneal limbus detection in 2D image space [16], but we developed a novel approach that uses a 3D eye model because the accuracy needs to be increased for GRP estimation. Figure 3 shows the tracking algorithm flow.…”

Section: Corneal Imaging Cameramentioning

confidence: 99%

Point of Gaze Estimation Using Corneal Surface Reflection and Omnidirectional Camera Image

Ogawa

Nakazawa

Nishida

2018

IEICE Trans. Inf. & Syst.

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Taishi OGAWA †a) , Atsushi NAKAZAWA †b) , Nonmembers, and Toyoaki NISHIDA †, † †c) , Member SUMMARYWe present a human point of gaze estimation system using corneal surface reflection and omnidirectional image taken by spherical panorama cameras, which becomes popular recent years. Our system enables to find where a user is looking at only from an eye image in a 360 • surrounding scene image, thus, does not need gaze mapping from partial scene images to a whole scene image that are necessary in conventional eye gaze tracking system. We first generate multiple perspective scene images from an omnidirectional (equirectangular) image and perform registration between the corneal reflection and perspective images using a corneal reflection-scene image registration technique. We then compute the point of gaze using a corneal imaging technique leveraged by a 3D eye model, and project the point to an omnidirectional image. The 3D eye pose is estimate by using the particle-filter-based tracking algorithm. In experiments, we evaluated the accuracy of the 3D eye pose estimation, robustness of registration and accuracy of PoG estimations using two indoor and five outdoor scenes, and found that gaze mapping error was 5.546 [deg] on average.

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Section: Corneal Imaging Cameramentioning

confidence: 99%

Point of Gaze Estimation Using Corneal Surface Reflection and Omnidirectional Camera Image

Ogawa

Nakazawa

Nishida

2018

IEICE Trans. Inf. & Syst.

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“…Although the result has shown an improvement towards the approach, the reliability of the eye images used in that experiment has been questions. Bowyer et al (2013) stated the eye image from CASIA database (CASIA version 1) is not reliable to be used as a primary dataset because the image has been edited to get a perfect circle on the pupil and iris. Moreover, the pupil color has been modified to become darker than a normal pupil.…”

Section: ) **Na: Not Availablementioning

confidence: 99%

Implementation of Geodesic Active Contour Approach for Pigment Spots Segmentation on the Iris Surface

Man

Jabal

Rahim

et al. 2016

Journal of Computer Science

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Abstract:The aim of this paper is to study the effectiveness of geodesic active contour approach to segment the pigment spots on iris surface. Pigment spot is an abnormal marker that appears on the iris surface, which may lead to eye diseases. The multi appearance, scattered location and dynamic shape of the pigment spots on the iris surface make them very difficult to detect using automatic segmentation method. Therefore, this paper presents the preliminary works that observe the efficiency and accuracy of geodesic active contour approach to automatically segment the pigment spots on iris surface. Miles database and "lu_wei_feb_2006" dataset are used in this study. The results of the conducted experiment show that geodesic active contour approach able to correctly and accurately detect up to 77% and 90% of the pigment spots in Miles database and "lu_wei_feb_2006" dataset, respectively.

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“…Methods mentioned above require hi ghquality iris images for proper functioning, so high precision hardware deployment and strict coordination with users are required. However, with extended use strategy of iris recognition system, less constrained or unconstrained iris recognition systems are becoming more and more necessary [9,10]. An unconstrained iris recognition system can relieve environmental restrictions and human cooperation restrictions [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Improving Unconstrained Iris Recognition Performance via Domain Adaptation Metric Learning Method

Yan¹,

Zhou²,

Tian³

2016

IJSIA

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To improve unconstrained iris recognition system performance in different environments, a performance improvement method of unconstrained iris recognition based on domain adaptation metric learning is proposed. A kernel matrix is calculated as the solution of domain adaptation metric learning. The known Hamming distance computing by intra-class and inter-class is used as the optimization learning constraints in the process of iris recognition. An optimal Mahalanobis matrix is computed for certain cross-environment system, then distance between two iris samples is redefined. The experimental results indicate that the proposed method can increase the accuracy of the unconstrained iris recognition in different circumstances, improving the classification ability of iris recognition system.

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A Survey of Iris Biometrics Research: 2008–2010

Cited by 20 publications

References 205 publications

Point of Gaze Estimation Using Corneal Surface Reflection and Omnidirectional Camera Image

Point of Gaze Estimation Using Corneal Surface Reflection and Omnidirectional Camera Image

Implementation of Geodesic Active Contour Approach for Pigment Spots Segmentation on the Iris Surface

Improving Unconstrained Iris Recognition Performance via Domain Adaptation Metric Learning Method

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