How iris recognition works

Daugman, John

doi:10.1109/icip.2002.1037952

Cited by 1,074 publications

(1,764 citation statements)

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“…Thus, determining the iris contours is not a trivial task, and several approaches have been applied. The most robust ones consist of operators acting as edge detectors applied along predetermined paths (i.e., circles for the inner and outer boundaries and arcs for the eyelids) based on contour integrals [19] or Hough transforms [75]. Fig.…”

Section: Iris Identiÿcationmentioning

confidence: 99%

“…The system proposed by J. Daugman [17] has gained a certain commercial relevance and, due to its technical interest, will be described here so as to provide a comparison reference with other systems. Preprocessing consists of the application of pseudo-polar coordinates (taking into account that the inner and outer circular boundaries may not be completely concentric) and standardizing the radial size of the iris (so that possible dilation of the pupil does not a ect the system) [19]. As for feature extraction, 2D Gabor wavelets [26] are used to perform a multiscale analysis of the iris [17,16,15].…”

Section: Iris Identiÿcationmentioning

confidence: 99%

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Biometric identification systems

et al. 2003

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In this paper, we provide an overview of the fundamentals of biometric identiÿcation, together with a description of the main biometric technologies currently in use, all of them within a common reference framework. A comparison on di erent qualitative parameters of these technologies is also given, so that the reader may have a clear perspective of advantages and disadvantages of each. A section on multibiometrics describes the state of the art in making these systems work coordinately. Fusion at di erent conceptual levels is described. Finally, a section on commercial issues provides the reader a perspective of the main companies currently involved in this ÿeld. ?

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Section: Iris Identiÿcationmentioning

confidence: 99%

Section: Iris Identiÿcationmentioning

confidence: 99%

Biometric identification systems

et al. 2003

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“…The iris is the annular part between the black pupil and the white sclera [1]. This intensity pattern is believed to have enough detailed information for personal identity verification.…”

Section: Introductionmentioning

confidence: 99%

“…A significant point in no-reference image blur detection is modeling blur features. Therefore, various meaningful blur features have been defined for iris image blur detection in related works [1], [5]- [8]. Daugman [1] used an 8 × 8 convolution kernel and extracted the total power in response as blur feature.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, various meaningful blur features have been defined for iris image blur detection in related works [1], [5]- [8]. Daugman [1] used an 8 × 8 convolution kernel and extracted the total power in response as blur feature. Kalka et al [9] employed a direction filter to calculate motion direction and then obtained motion length as blur feature.…”

Section: Introductionmentioning

confidence: 99%

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Iris Image Blur Detection with Multiple Kernel Learning

Pan

Xie

Mao

2012

IEICE Trans. Inf. & Syst.

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SUMMARYIn this letter, we analyze the influence of motion and outof-focus blur on both frequency spectrum and cepstrum of an iris image. Based on their characteristics, we define two new discriminative blur features represented by Energy Spectral Density Distribution (ESDD) and Singular Cepstrum Histogram (SCH). To merge the two features for blur detection, a merging kernel which is a linear combination of two kernels is proposed when employing Support Vector Machine. Extensive experiments demonstrate the validity of our method by showing the improved blur detection performance on both synthetic and real datasets.

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Pattern Recognition Letters

2005

Encyclopedia of Statistical Sciences

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited.In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright

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How iris recognition works

Cited by 1,074 publications

References 6 publications

Biometric identification systems

Biometric identification systems

Iris Image Blur Detection with Multiple Kernel Learning

Pattern Recognition Letters

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