Algorithm for Iris recognition based on contourlet Transform and Entropy

Ezzaki, Ayoub; Idrissi, Nouhaila; Moreno, Francisco-Ángel; Masmoudi, Lhoussaine

doi:10.5565/rev/elcvia.1190

Cited by 3 publications

(1 citation statement)

References 39 publications

(39 reference statements)

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“…Image texture-based methodology is a relatively recent approach proposed by Li Ma [23]. Further several other methodologies like Gray Level Co-occurrence Matrix (GLCM) [24] include Auto Correlation Function (ACF), Local Binary Pattern [25], Histogram Pattern [26], Haar wavelet [27], Contourlet [28] and Texture code [29] have been identified for iris recognition purpose. To measure the consistency of the iris image of the same eye, two dimensional correlation filters were used by Kumar et.al.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of New Feature based on Ordinal Pattern Analysis for Iris Biometric Recognition

Sheena¹,

Mathew²,

Krishna³

2022

IJACSA

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The Iris recognition technique is currently the most efficient biometric identification system and is a common system on the practical front. Though most of the commercial systems use the patented Daugman's algorithm, which mainly uses wavelet-based features, research is still active in identifying novel features that can provide personal identification. Here the first novel proposal of using ordinal pattern measure based on nonlinear time series analysis is put forth to characterize the unique pattern of the iris of individuals and thereby perform personal identification. Dispersion Entropy is a nonlinear timeseries analysis method highly efficient in the characterization of the complexity of any data series with proven effectiveness in the characterization of model system dynamics as well as real-world data series. The results show that dispersion entropy can be used to identify iris images of specific individuals. The efficiency of this method is evaluated by computing correlation and RMSE between dispersion entropy values of normalized iris image rubber sheet data. The experimental results on the popular IRIS database-CASIA v1-demonstrate that the proposed method can effectively perform differential identification of iris images from different individuals. The results specifically indicate that the density of information along the angular direction of iris images which falls along the rows of rubber sheet data. This can be efficiently utilized with the method or ordinal pattern characterization and proves to be having promising potential for being incorporated into biometrics personal identification systems.

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Section: Related Workmentioning

confidence: 99%

Performance Evaluation of New Feature based on Ordinal Pattern Analysis for Iris Biometric Recognition

Sheena¹,

Mathew²,

Krishna³

2022

IJACSA

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Integrating handcrafted features with deep convolutional neural network and BWOA optimization for improved postmortem iris recognition system

Kaur,

Saini

2024

Soft Comput

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Iris recognition method based on segmentation

Mahameed¹,

Ahmed²,

Abdullah³

2022

Eureka: PE

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The development of science and studies has led to the creation of many modern means and technologies that focused and directed their interests on enhancing security due to the increased need for high degrees of security and protection for individuals and societies. Hence identification using a person's vital characteristics is an important privacy topic for governments, businesses and individuals. A lot of biometric features such as fingerprint, facial measurements, acid, palm, gait, fingernails and iris have been studied and used among all the biometrics, in particular, the iris gets the attention because it has unique advantages as the iris pattern is unique and does not change over time, providing the required accuracy and stability in verification systems. This feature is impossible to modify without risk. When identifying with the iris of the eye, the discrimination system only needs to compare the data of the characteristics of the iris of the person to be tested to determine the individual's identity, so the iris is extracted only from the images taken. Determining correct iris segmentation methods is the most important stage in the verification system, including determining the limbic boundaries of the iris and pupil, whether there is an effect of eyelids and shadows, and not exaggerating centralization that reduces the effectiveness of the iris recognition system. There are many techniques for subtracting the iris from the captured image. This paper presents the architecture of biometric systems that use iris to distinguish people and a recent survey of iris segmentation methods used in recent research, discusses methods and algorithms used for this purpose, presents datasets and the accuracy of each method, and compares the performance of each method used in previous studies

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Algorithm for Iris recognition based on contourlet Transform and Entropy

Cited by 3 publications

References 39 publications

Performance Evaluation of New Feature based on Ordinal Pattern Analysis for Iris Biometric Recognition

Performance Evaluation of New Feature based on Ordinal Pattern Analysis for Iris Biometric Recognition

Integrating handcrafted features with deep convolutional neural network and BWOA optimization for improved postmortem iris recognition system

Iris recognition method based on segmentation

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