Feasibility of Hough-transform-based iris localisation for real-time-application

Toennies, Klaus D.; Behrens, F.; Aurnhammer, Melanie

doi:10.1109/icpr.2002.1048486

Cited by 21 publications

(14 citation statements)

References 12 publications

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“…Initial algorithms (like Xie et al, 1994) were based on edge images, while a good edge image is hard to get under uncontrolled lightning when the eye contrast is low. Toennies et al (2002) applied Generalized Hough Transform to detect and track eyes. Feng and Yuen (2001) use three cues to detect eyes: the intensity (eye intensity is relatively low), the estimated direction of the line joining the eye centers and the result of the convolution of the image by an eye variance filter.…”

Section: Overview Of Existing Methodsmentioning

confidence: 99%

Real-time facial feature localization by combining space displacement neural networks

Hanif

Prévost

Belaroussi

et al. 2008

Pattern Recognition Letters

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Section: Overview Of Existing Methodsmentioning

confidence: 99%

Real-time facial feature localization by combining space displacement neural networks

Hanif

Prévost

Belaroussi

et al. 2008

Pattern Recognition Letters

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“…• Toennies et al [36] showed how the HT can be used to localise the irises of human eyes for real-time applications, one advantage of the transform being its capability for coping with partial occlusion -a factor that is often quite serious with irises.…”

Section: Perspective On the Development And Use Of The Hough Transformmentioning

confidence: 99%

Object Location Using the HOUGH Transform

Davies¹

2012

Machine Vision Handbook

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“…The Hough Transform was used to detect the eye sockets. It is used in this kind of tasks, but most often in visible light [16,28,30,34]. The biggest disadvantages of this method, that is, problems with the selection of ellipse sizes and with their analysis in the case of rotation, were here eliminated.…”

Section: Detection Of the Eye Socket Areasmentioning

confidence: 99%

Automatic method for detection of characteristic areas in thermal face images

Marzec

Koprowski

Wróbel

et al. 2013

Multimed Tools Appl

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The use of thermal images of a selected area of the head in screening systems, which perform fast and accurate analysis of the temperature distribution of individual areas, requires the use of profiled image analysis methods. There exist methods for automated face analysis which are used at airports or train stations and are designed to detect people with fever. However, they do not enable automatic separation of specific areas of the face. This paper presents an algorithm for image analysis which enables localization of characteristic areas of the face in thermograms. The algorithm is resistant to subjects' variability and also to changes in the position and orientation of the head. In addition, an attempt was made to eliminate the impact of background and interference caused by hair and hairline. The algorithm automatically adjusts its operation parameters to suit the prevailing room conditions. Compared to previous studies (Marzec et al., J Med Inform Tech 16:151-159, 2010), the set of thermal images was expanded by 34 images. As a result, the research material was a total of 125 patients' thermograms performed in the Department of Pediatrics and Child and Adolescent Neurology in Katowice, Poland. The images were taken interchangeably with several thermal cameras: AGEMA 590 PAL (sensitivity of 0.1°C), ThermaCam S65 (sensitivity of 0.08°C), A310 (sensitivity of 0.05°C), T335 (sensitivity of 0.05°C) with a 320×240 pixel optical resolution of detectors, maintaining the principles related to taking thermal images for medical thermography. In comparison to (Marzec et al., J Med Inform Tech 16:151-159, 2010), the approach presented there has been extended and modified. Based on the comparison with other methods presented in the literature, it was demonstrated that this method is more complex as it enables to determine the approximate areas of selected parts of Multimed Tools Appl (2015)

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Feasibility of Hough-transform-based iris localisation for real-time-application

Abstract: We present a fast method for locating iris

Cited by 21 publications

References 12 publications

Real-time facial feature localization by combining space displacement neural networks

Real-time facial feature localization by combining space displacement neural networks

Object Location Using the HOUGH Transform

Automatic method for detection of characteristic areas in thermal face images

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