Iris recognition technology

Williams, George

doi:10.1109/ccst.1996.551842

Cited by 11 publications

(8 citation statements)

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“…The overall match score for each iris comparison is then computed using (4); no belief propagation is done, and the equations in Section 3.5 are not used since we essentially have only one subregion. This method is close to that of Daugman reported in [3], which is considered to be a standard iris matching approach [12]; the difference is that the parameter values used for the Gabor wavelets may be different. Because the values used by Daugman were not specified, we chose values to optimize performance of the baseline method.…”

Section: Experimental Setup and Resultsmentioning

confidence: 63%

Graphical Model Approach to Iris Matching Under Deformation and Occlusion

Kerekes

Balakrishnan

Thornton

et al. 2007

2007 IEEE Conference on Computer Vision and Pattern Recognition

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Section: Experimental Setup and Resultsmentioning

confidence: 63%

Graphical Model Approach to Iris Matching Under Deformation and Occlusion

Kerekes

Balakrishnan

Thornton

et al. 2007

2007 IEEE Conference on Computer Vision and Pattern Recognition

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“…Daugman [7] pioneered this type of feature extraction for irises, using the phase response of Gabor filters. His early success has popularized this recognition algorithm as a standard in the field, including attempts at practical implementation [8].…”

Section: Introductionmentioning

confidence: 99%

Robust Iris Recognition Using Advanced Correlation Techniques

Thornton

Savvides

Kumar

2005

Lecture Notes in Computer Science

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Abstract. The iris is considered one of the most reliable and stable biometrics as it is believed to not change significantly during a person's lifetime. Standard techniques for iris recognition, popularized by Daugman, apply Gabor wavelet analysis for feature extraction. In this paper, we consider an alternative method for iris recognition, the use of advanced distortion-tolerant correlation filters for robust pattern matching. These filters offer two primary advantages: shift invariance, and the ability to tolerate within-class image variations. The iris images we use in our experiments are from the CASIA database and also from an iris database we collected at CMU. In this paper, we perform automatic segmentation of the iris (which surrounds the pupil) from the rest of the eye, normalizing for scale and pupil dilation. We then use these segmented iris images to compare the recognition performance of various methods, including Gabor wavelet feature extraction, to correlation filters.

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“…Daugman implemented the first prototype of an operating iris recognition system at Cambridge University in 1993 [17]. Daugman proposed to utilize circular zones for iris alignment, and two-dimensional Gabor filters for multiscale representation of the iris pattern, using the filter parameters to construct a codeword which is then compared using a Hamming distance criterion for a similarity measure.…”

Section: Introductionmentioning

confidence: 99%

Nonintrusive Iris Image Extraction for Iris Recognition-Based Biometric Identification

Ertürk

2006

Circuits Syst Signal Process

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This paper proposes a new approach to obtain iris images without requiring the person to look directly into the camera. Most biometric identification methods using iris recognition assume that the eye image of the person is available, and are only concerned with the extraction of the iris from the eye image. These methods require the person to look directly into the camera, which is a rather uncomfortable process. In this paper, a robust approach is proposed to initially locate eye regions within facial images taken from a distance. Then the iris image is extracted from the detected regions. Hence, a nonintrusive and more comfortable iris imaging approach has been implemented.

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Iris recognition technology

Cited by 11 publications

References 1 publication

Graphical Model Approach to Iris Matching Under Deformation and Occlusion

Graphical Model Approach to Iris Matching Under Deformation and Occlusion

Robust Iris Recognition Using Advanced Correlation Techniques

Nonintrusive Iris Image Extraction for Iris Recognition-Based Biometric Identification

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