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Proceedings ArticleDOI

Efficient iris recognition method for identification

04 Dec 2009-pp 1-6
TL;DR: Two different approaches for iris identification viz. template matching and average value method are presented.
Abstract: In recent times the security has become a major issue of concern among the people. There is multifold increase in the installation and deployment of security services around the world. The threat starts when an unwanted person tries to obtain access to any place or value. In such a scenario the correct identification of the person is necessary so as to restrict the unidentified people from gaining access. Biometric details of a person offer the possibility of identifying the person very accurately. Iris identification is one such technique. This paper presents two different approaches for iris identification viz. template matching and average value method. Both the methods were tested on variety of images from the available database [1].Template matching is done using NI Vision Assistant 7.1 and NI LabVIEW 7.1. MATLAB 7.4 was used to implement average value method.
Citations
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Book ChapterDOI
01 Jan 2013
TL;DR: This new survey is intended to update the previous one, and covers iris biometrics research over the period of roughly 2008–2010, and lists a larger number of references than the inception-through-2007 survey.
Abstract: A recent survey of iris biometric research from its inception through 2007, roughly 15 years of research, lists approximately 180 publications. This new survey is intended to update the previous one, and covers iris biometrics research over the period of roughly 2008–2010. Research in iris biometrics has expanded so much that, although covering only 3 years and intentionally being selective about coverage, this new survey lists a larger number of references than the inception-through-2007 survey.

151 citations


Cites methods from "Efficient iris recognition method f..."

  • ...[53] propose a method that uses a grid on the iris image and a vector of the average pixel values in the elements of the grid for representing and matching the iris texture....

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Journal ArticleDOI
TL;DR: This paper reviews the state-of-the-art design and implementation of iris-recognition-at-a-distance (IAAD) systems and presents a complete solution to the design problem of an IAAD system, from both hardware and algorithmic perspectives.

133 citations

Proceedings ArticleDOI
11 Oct 2011
TL;DR: A smart camera, LabVIEW and vision software tools are utilized to generate eye detection and tracking algorithms and the implemented algorithms and performance results of these algorithms on the smart camera are presented.
Abstract: Real-time eye and iris tracking is important for handsoff gaze-based password entry, instrument control by paraplegic patients, Internet user studies, as well as homeland security applications. In this project, a smart camera, LabVIEW and vision software tools are utilized to generate eye detection and tracking algorithms. The algorithms are uploaded to the smart camera for on-board image processing. Eye detection refers to finding eye features in a single frame. Eye tracking is achieved by detecting the same eye features across multiple image frames and correlating them to a particular eye. The algorithms are tested for eye detection and tracking under different conditions including different angles of the face, head motion speed, and eye occlusions to determine their usability for the proposed applications. This paper presents the implemented algorithms and performance results of these algorithms on the smart camera.

41 citations


Cites background from "Efficient iris recognition method f..."

  • ...Many existing systems require PCs for vision processing [1], [2]....

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Journal ArticleDOI
TL;DR: A new learning-based method is proposed to define and extract rotation- and illumination-invariant main local patterns associated with the iris texture, and the metric-learning-based transform is employed to improve the discrimination of these patterns in recognition process.
Abstract: Biometric-based authentication system is one of the main strategies to protect and control the access of users to important resources in any system and organization. Iris pattern is one of the best and most reliable biological features used in these systems. Extraction of high-discriminative local features can increase the recognition accuracy of iris-based biometric systems, especially when the number of users is high. Most of the existing methods utilize a combination of simple handcraft local feature models that deteriorate system performance when the number of users is increased. In this paper, after identification and segmentation of iris region, a new learning-based method is proposed to define and extract rotation- and illumination-invariant main local patterns associated with the iris texture. Afterwards, the metric-learning-based transform is employed to improve the discrimination of these patterns in recognition process. The proposed method was applied on more than 10,000 images from CASIA-V4, UBIRIS and ICE data sets. The identification accuracy of this method is 99.7, 98.13 and 99.26%, respectively, that is, higher than other methods in terms of both recognition accuracy and the number of used images.

7 citations

References
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Journal ArticleDOI

37,017 citations


"Efficient iris recognition method f..." refers background in this paper

  • ...Using Ostsu’s rule [10] the value for the threshold can be determined....

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Journal ArticleDOI
TL;DR: A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence, which implies a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates.
Abstract: A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of a person's iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coefficients, whose most-significant bits comprise a 256-byte "iris code". Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons imply a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally to a conditional false accept probability of one in about 10/sup 31/. >

3,399 citations


"Efficient iris recognition method f..." refers background in this paper

  • ...Among these biometric technologies, iris recognition is one of the best measures for person identification [8], [9]....

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Journal ArticleDOI
01 Sep 1997
TL;DR: This paper examines automated iris recognition as a biometrically based technology for personal identification and verification from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric assessment.
Abstract: This paper examines automated iris recognition as a biometrically based technology for personal identification and verification. The motivation for this endeavor stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric assessment. In particular the biomedical literature suggests that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body, its appearance is amenable to remote examination with the aid of a machine vision system. The body of this paper details issues in the design and operation of such systems. For the sake of illustration, extant systems are described in some amount of detail.

2,046 citations

Proceedings Article
16 Feb 2007
TL;DR: Iris recognition as one of the important method of biometrics-based identification systems and iris recognition algorithm is described and experimental results show that the proposed method has an encouraging performance.
Abstract: In this paper, iris recognition as one of the important method of biometrics-based identification systems and iris recognition algorithm is described. As technology advances and information and intellectual properties are wanted by many unauthorized personnel. As a result many organizations have being searching ways for more secure authentication methods for the user access. In network security there is a vital emphasis on the automatic personal identification. Due to its inherent advantages biometric based verification especially iris identification is gaining a lot of attention. Iris recognition uses iris patterns for personnel identification. The system steps are capturing iris patterns; determining the location of iris boundaries; converting the iris boundary to the stretched polar coordinate system; extracting iris code based on texture analysis. The system has been implemented and tested using dataset of number of samples of iris data with different contrast quality. The developed algorithm performs satisfactorily on the images, provides 93% accuracy. Experimental results show that the proposed method has an encouraging performance.

1,389 citations


"Efficient iris recognition method f..." refers methods in this paper

  • ...Hough transform is used to find such a circle [7]....

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Journal ArticleDOI
TL;DR: The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris.
Abstract: Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2 255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.

999 citations


"Efficient iris recognition method f..." refers background in this paper

  • ...Among these biometric technologies, iris recognition is one of the best measures for person identification [8], [9]....

    [...]