Iris code matching using adaptive Hamming distance

Dehkordi, Arezou Banitalebi; Abu-Bakar, S. A. R.

doi:10.1109/icsipa.2015.7412224

Cited by 9 publications

(4 citation statements)

References 11 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%

“…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. The expanding and adjoining behavior of Hamming subsets to the right or left neighboring bits increased the accuracy of Hamming distance computation.…”

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

“…We cannot compute distance metrics across observations with different numbers of features, and it Scientific Programming is pointless to do so if the number of features is the same but the actual features are different. Adaptive HD [41] was used in Iris code matching, thereby improving the performance of Iris code matching.…”

Section: Hamming Distance (Hd)mentioning

confidence: 99%

Metrics Space and Norm: Taxonomy to Distance Metrics

Subramanian

Paul

Kim

et al. 2022

Scientific Programming

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A lot of machine learning algorithms, including clustering methods such as K-nearest neighbor (KNN), highly depend on the distance metrics to understand the data pattern well and to make the right decision based on the data. In recent years, studies show that distance metrics can significantly improve the performance of the machine learning or deep learning model in clustering, classification, data recovery tasks, etc. In this article, we provide a survey on widely used distance metrics and the challenges associated with this field. The most current studies conducted in this area are commonly influenced by Siamese and triplet networks utilized to make associations between samples while employing mutual weights in deep metric learning (DML). They are successful because of their ability to recognize the relationships among samples that show a similarity. Furthermore, the sampling strategy, suitable distance metric, and network structure are complex and difficult factors for researchers to improve network model performance. So, this article is significant because it is the most recent detailed survey in which these components are comprehensively examined and valued as a whole, evidenced by assessing the numerical findings of the techniques.

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“…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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Iris code matching using adaptive Hamming distance

Cited by 9 publications

References 11 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

Metrics Space and Norm: Taxonomy to Distance Metrics

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

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