Efficient iris localization and recognition

Soliman, Naglaa F.; Mohamed, Essam E. M.; Fikri, Magdi; El‐Samie, Fathi E. Abd; Abd-Elnaby, Mustafa M.

doi:10.1016/j.ijleo.2016.11.150

Cited by 40 publications

(13 citation statements)

References 8 publications

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“…we use a statistical method to account eyelashes presented in litirature [12] and 1D Log-Gabor Filter features to uniquely identify iris. We have studied various well known algorithms for iris recognition [29], [32], [33], [34], [35], [36], [37] and compared the results with state-of-the art algorithms. Fig.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Rai et al [35] used Support Vector Machine (SVM), Hamming Distance, parabola detection and trimmed median filter to detect and remove the eyelid, and eyelashes from the iris image. Soliman et al [36] used a coarse-to-fine algorithm to address the computational cost problem and the integrodifferential operator to get information aboutpupil centers and radii. In [37] Dehkordi et al used Adaptive Hamming Distance to improve the performance of iris code matching stage.…”

Section: Related Workmentioning

confidence: 99%

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Efficient Iris Pattern Recognition Method by using Adaptive Hamming Distance and 1D Log-Gabor Filter

Tobji¹,

Wu²,

Ayoub³

et al. 2018

ijacsa

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Iris recognition is one of the highly reliable security methods as compared to the other bio-metric security techniques. The iris is an internal organ whose texture is randomly determined during embryonic gestation and is amenable with a computerized machine vision system for the remote examination. Previously, researchers utilized different approaches like Hamming Distance in their iris recognition algorithms. In this paper, we propose a new method to improve the performance of the iris recognition matching system. Firstly, 1D Log-Gabor Filter is used to encode the unique features of iris into the binary template. The efficiency of the algorithm can be increased by taking into account the coincidence fragile bit's location with 1D Log-Gabor filter. Secondly, Adaptive Hamming Distance is used to examine the affinity of two templates. The main steps of proposed iris recognition algorithm are segmentation by using the Hough's circular transformation method, normalization by Daugman's rubber sheet model that provides a high percentage of accuracy, feature encoding and matching. Simulation studies are made to test the validity of the proposed algorithm. The results obtained ensure the superior performance of our algorithm against several state-of-the-art iris matching algorithms. Experiments are performed on the CASIA V1.0 iris database, the success of the proposed method with a genuine acceptance rate is 99.92%.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Efficient Iris Pattern Recognition Method by using Adaptive Hamming Distance and 1D Log-Gabor Filter

Tobji¹,

Wu²,

Ayoub³

et al. 2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…For the characterization of the iris, the most useful methods are the Gabor wavelet transform applied by Daugman, the Gabor filter [24], the Laplacian pyramid [25], and orientable pyramid transform method [26]. We have studied various well-known algorithms for iris recognition [17,18,27,[29][30][31][32][33][34][35] and propose a new method for iris detection based on the fusion of FLDA/PCA. We also employ 1D Log-Gabor filter and used hamming distance for comparison between two iris templates.…”

Section: Related Workmentioning

confidence: 99%

A Synthetic Fusion Rule Based on FLDA and PCA for Iris Recognition Using 1D Log‐Gabor Filter

Tobji

Ayoub

2019

Mathematical Problems in Engineering

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Iris recognition is one of the most useful methods to identify or verify people in biometric recognition systems. Iris patterns contain many features that distinguish people from each other. In this paper, a novel iris recognition method is proposed based on the fusion of Fisher Linear Discriminate Analysis (FLDA) with embedding Principal Component Analysis (PCA) method. In this work, firstly we use 1D Log-Gabor to elicit the iris features from an approximation part. Secondly, we obtain an appropriate degree of clarity for the iris with fusion of FLDA/PCA to eliminate the optical reflections on the iris image. Experiments of our proposed algorithm are performed on the CASIA V1 database. The results of our proposed approach show a good performance with recognition rate up to 99.99%.

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“…Finally, a refinement step is performed using an integrodifferential operator to obtain the centers and the final rays of the iris and the pupil. This system is robust against occlusions and intensity variations [65]. Naseem et al (2017) proposed an algorithm to compare the vanguard spatial representation classification with Bayesian fusion for several sectors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They proposed two algorithms: First, a new method to eliminate the noise of the iris image and, second, a method to extract the characteristics of the texture through a combined approach of the local binary model and the gray level cooccurrence matrix. The proposed approach provided the highest recognition rate of 96.5% and low error rate and required less uptime[64] Soliman et al (2017). introduced a rough algorithm to solve the computational cost problem while achieving an acceptable precision.…”

mentioning

confidence: 99%

Efficient Biometric Iris Recognition Based on Iris Localization Approach

Al-Ani

2019

UHD J SCI TECH

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Biometric recognition is an emerging technology that has attracted more attention in recent years. This issue uses physiological and behavioral characteristics to identify an individual. The iris recognition approaches are among the most accurate biometric technologies with immense potential for use in global security applications. The purpose of this research is to implement an efficient approach to treat the diseased eye images in order to verify the second iris examination for the same person by inserting an improvement step to the iris recognition system. This step makes a correction of boundary around pupil and removes fraction regions. This approach demonstrates its ability to detect the inner limit of the iris. The obtained results shows that 90% success in the detection diseased eye images, which makes the iris recognition system more accurate and safe.

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Efficient iris localization and recognition

Cited by 40 publications

References 8 publications

Efficient Iris Pattern Recognition Method by using Adaptive Hamming Distance and 1D Log-Gabor Filter

Efficient Iris Pattern Recognition Method by using Adaptive Hamming Distance and 1D Log-Gabor Filter

A Synthetic Fusion Rule Based on FLDA and PCA for Iris Recognition Using 1D Log‐Gabor Filter

Efficient Biometric Iris Recognition Based on Iris Localization Approach

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