Topography-Based Detection of the Iris Centre Using Multiple-Resolution Images

Ponz, Victoria; Villanueva, Arantxa; Sesma-Sanchez, Laura; Ariz, Mikel; Cabeza, Rafael

doi:10.1109/imvip.2011.15

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Among the non-curvature-based methods, algorithms based on topographical characteristics try to label each pixel according to grey level changes in the pixel neighborhood [Ponz et al 2011]. The patterns of these topographic labels capture information about the original three-dimensional object in the scene and about the illumination [Pong et al 1985].…”

Section: Introductionmentioning

confidence: 99%

Hybrid method based on topography for robust detection of iris center and eye corners

Villanueva

Ponz

Sesma-Sanchez

et al. 2013

ACM Trans. Multimedia Comput. Commun. Appl.

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A multistage procedure to detect eye features is presented. Multiresolution and topographic classification are used to detect the iris center. The eye corner is calculated combining valley detection and eyelid curve extraction. The algorithm is tested in the BioID database and in a proprietary database containing more than 1200 images. The results show that the suggested algorithm is robust and accurate. Regarding the iris center our method obtains the best average behavior for the BioID database compared to other available algorithms. Additional contributions are that our algorithm functions in real time and does not require complex post processing stages. ACM Reference Format:Villanueva, A., Ponz, V., Sesma-Sanchez, L., Ariz, M., Porta, S., and Cabeza, R. 2013. Hybrid method based on topography for robust detection of iris center and eye corners. ACM Trans. Multimedia Comput. Commun.

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Section: Introductionmentioning

confidence: 99%

Hybrid method based on topography for robust detection of iris center and eye corners

Villanueva

Ponz

Sesma-Sanchez

et al. 2013

ACM Trans. Multimedia Comput. Commun. Appl.

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“…gions are not detected for a number of seconds during the driving, and we have integrated an iris center detector [4] as a requirement for further investigation in gaze estimation in the driving scenario.…”

Section: Discussionmentioning

confidence: 99%

“…The Viola-Jones detection method is based on a training procedure consisting of a learning stage. Eye detection based on this method is typically performed in stages that first attempt to detect the face and then attempt to locate the eye in the previously segmented face region [4]. However, other approaches apply Viola-Jones eye detectors to locate eye-like elements in the entire image without the face area constraint.…”

Section: Introductionmentioning

confidence: 99%

Hybrid eye detection algorithm for outdoor environments

Bengoechea

Villanueva

Cabeza

2012

Proceedings of the 2012 ACM Conference on Ubiquitous Computing

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When performing eye detection in a driving scenario, new challenges arise that do not occur in a standard indoor eye tracking session. Rapid subject movement, non-controlled fast light variation and partial or total occlusions are the main problems that must be overcome. Furthermore, sunlight's infrared component makes it difficult the use of active artificial infrared light sources. In this paper, we describe a novel algorithm that combines Viola Jones face detector and TLD (Tracking Learning Detection) algorithm. In a standard driving scenario, it achieves a 84% rate of detection. Furthermore, we have designed a filtering stage that allows a low false positive rate. The algorithms hardware requirement is a standard web cam, and it can potentially work in real time.This section is limited to the following 16 terms and MUST be included on the first page of all submissions after the ACM Categories section, then as well chosen properly on the Proceedings or Publication's submission page: Algorithms,

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