A fusion approach to unconstrained iris recognition

Santos, Gil; Hoyle, Edmundo

doi:10.1016/j.patrec.2011.08.017

Cited by 85 publications

(43 citation statements)

References 13 publications

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“…A fusion approach to unconstrained iris recognition has been proposed. 18 Other recent approaches to iris pattern recognition include the independent component analysis approach by Huang, 5 the texture analysis using multichannel Gabor filtering and wavelet transform by Zhu et al, 19 the selforganizing neural network approach by Lim et al 20 and the use of a partial iris for recognition using 1-D approach by Du et al 21 These methods calculate the enrolment iris template by averaging various iris signatures for each iris pattern. Can majority vote techniques compute a more precise enrolment iris template than averaging various iris signatures?…”

Section: Related Workmentioning

confidence: 99%

Iris pattern recognition based on cumulative sums and majority vote methods

Viriri

Tapamo

2017

International Journal of Advanced Robotic Systems

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Iris recognition is proving to be one of the most reliable biometric authentication systems. Since biometric authentication systems store multiple templates per user, there is need to optimize their computational and storage complexities by creating a single reliable specimen iris template per user. In this article, we propose two majority vote-based strategies for the creation of a single iris template per user. An algorithm based on cumulative-sums grey change analysis is used to extract the iris texture features for template creation. The experimental results on the CASIA and UBIRIS iris databases show that the approach is effective and efficient, with a recognition rate of 99.6%.

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Section: Related Workmentioning

confidence: 99%

Iris pattern recognition based on cumulative sums and majority vote methods

Viriri

Tapamo

2017

International Journal of Advanced Robotic Systems

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“…In 2012 Gil Santos, Edmundo Hoyle [11] The recognition techniques used in this work performed by five steps: first, iris boundary detection, by following procedure: construct binary for the iris information, A contour is extracted from the white region of binary image, Hough transform is applied to obtain the circle for the iris region. Convert the eye image to grayscale and enhance it using Canny edge detection which applied inside the circular region finally, a Hough transform is used on the resulting to obtain the circle of the pupil.…”

Section: Literature Surveymentioning

confidence: 99%

Integrate Liveness Detection with Iris Verification to Construct Support Biometric System

Ahmed¹,

Abdulkareem²

2016

JCC

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The probabilities for the technology to be spoofed are widely acknowledged in biometric verification system. Important efforts have been conducted to study such threats and to develop countermeasures to direct attacks to the biometric verification system to ensure the security of these systems against spoof attacks and reduce this risk, by using another module that is added to the biometric verification system called the "liveness detection" which uses different anatomical properties to distinguish between real and fake traits. Thus, the robustness of the system against direct attacks can be improved through increasing the security level offered to the final user. This paper is an attempt to construct support biometric security system to protect the iris biometric verification system from spoof attacks, through integrating the iris verification system with addition module called liveness detection which composed of two sub-modules (static and dynamic). A test has been performed, for iris verification phase performed on two types of database (MMU DB) for 180 samples and (CASIA DB) for 90 samples, and gave accuracy (99.44%) with FAR of (0.0277) and FRR (0.0055) for MMU DB, and accuracy (97.77%) with FAR of (0.0333) and FRR (0.0222) for CASIA DB.

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“…Methods mentioned above require hi ghquality iris images for proper functioning, so high precision hardware deployment and strict coordination with users are required. However, with extended use strategy of iris recognition system, less constrained or unconstrained iris recognition systems are becoming more and more necessary [9,10]. An unconstrained iris recognition system can relieve environmental restrictions and human cooperation restrictions [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Improving Unconstrained Iris Recognition Performance via Domain Adaptation Metric Learning Method

Yan¹,

Zhou²,

Tian³

2016

IJSIA

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To improve unconstrained iris recognition system performance in different environments, a performance improvement method of unconstrained iris recognition based on domain adaptation metric learning is proposed. A kernel matrix is calculated as the solution of domain adaptation metric learning. The known Hamming distance computing by intra-class and inter-class is used as the optimization learning constraints in the process of iris recognition. An optimal Mahalanobis matrix is computed for certain cross-environment system, then distance between two iris samples is redefined. The experimental results indicate that the proposed method can increase the accuracy of the unconstrained iris recognition in different circumstances, improving the classification ability of iris recognition system.

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A fusion approach to unconstrained iris recognition

Cited by 85 publications

References 13 publications

Iris pattern recognition based on cumulative sums and majority vote methods

Iris pattern recognition based on cumulative sums and majority vote methods

Integrate Liveness Detection with Iris Verification to Construct Support Biometric System

Improving Unconstrained Iris Recognition Performance via Domain Adaptation Metric Learning Method

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