Human eye iris recognition using the mutual information

Dobeš, Michal; Machala, Lubomír; Tichavský, Petr; Pospíšil, Jiří

doi:10.1078/0030-4026-00388

Cited by 82 publications

(41 citation statements)

References 14 publications

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“…In [7] the results are between 98 % and 99 % by working with CASIA database (2255 samples from 213 subjects). In [3] best results are in the order of 99.05 % with a database of 384 images from 64 persons. None of these works specify the quality of databases, so a direct comparison is not possible.…”

Section: Comparison To Previous Workmentioning

confidence: 77%

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An Application of ARX Stochastic Models to Iris Recognition

Garza-Castañón

2006

Professional Practice in Artificial Intelligence

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Abstract. We present a new approach for iris recognition based on stochastic autoregressive models with exogenous input (ARX). Iris recognition is a method to identify persons, based on the analysis of the eye iris. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. The main contribution in this work is given in the step of characterization of iris features by using ARX models. In our work every iris in database is represented by an ARX model learned from data. In the comparison and matching step, data taken from iris sample are substituted into every ARX model and residuals are generated. A decision of accept or reject is taken based on residuals and on a threshold calculated experimentally. We conduct experiments with two different databases. Under certain conditions, we found a rate of successful identifications in the order of 99.7 % for one database and 100 % for the other.

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Section: Comparison To Previous Workmentioning

confidence: 77%

“…The approach is based on the fusion of edge and region information. In [3] an iris recognition approach based on mutual information is developed. In that work, couples of iris samples are geometrically aligned by maximizing their mutual information and subsequently recognized.…”

Section: Introductionmentioning

confidence: 99%

An Application of ARX Stochastic Models to Iris Recognition

Garza-Castañón

2006

Professional Practice in Artificial Intelligence

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“…The first database was CASIA [2], coming from the Chinese Academy of Sciences, Institute of Automation, while the second IrisBath [24] was developed at the University of Bath. We have also obtained an access to UBIRIS v.2.0 database [22] and the database prepared by Michael Dobeš and Libor Machala [7].…”

Section: Acquisition Of Iris Images and Iris Databasesmentioning

confidence: 99%

Selection of parameters in iris recognition system

Marciniak

Dąbrowski

Chmielewska

et al. 2012

Multimed Tools Appl

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This paper presents the detailed analysis of implementation issues occurred during preparation of the novel iris recognition system. First, we shortly describe the currently available acquisition systems and databases of iris images, which were used for our tests. Next, we concentrate on the feature extraction and coding with the execution time analysis. Results of the average execution time of loading the image, segmentation, normalization, and feature encoding, are presented. Finally, DET plots illustrate the recognition accuracy for IrisBath database.

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“…The database used in this work is both the Chinese Academy of Sciences-Institute of Automation-CASIA (Version 1 and Version 3) database and the UPOL database [42][43][44][45][46][47][48]. The Chinese Version 1 database used in the experimentation consists of 756 different iris images from the CASIA iris image database [7].…”

Section: Image Databasementioning

confidence: 99%

Iris Recognition Using Ridgelets

Birgale¹,

Kokare²

2012

Journal of Information Processing Systems

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Abstract-Image feature extraction is one of the basic works for biometric analysis. This paper presents the novel concept of application of ridgelets for iris recognition systems. Ridgelet transforms are the combination of Radon transforms and Wavelet transforms. They are suitable for extracting the abundantly present textural data that is in an iris. The technique proposed here uses the ridgelets to form an iris signature and to represent the iris. This paper contributes towards creating an improved iris recognition system. There is a reduction in the feature vector size, which is 1X4 in size. The False Acceptance Rate (FAR) and False Rejection Rate (FRR) were also reduced and the accuracy increased. The proposed method also avoids the iris normalization process that is traditionally used in iris recognition systems. Experimental results indicate that the proposed method achieves an accuracy of 99.82%, 0.1309% FAR, and 0.0434% FRR.

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Human eye iris recognition using the mutual information

Cited by 82 publications

References 14 publications

An Application of ARX Stochastic Models to Iris Recognition

An Application of ARX Stochastic Models to Iris Recognition

Selection of parameters in iris recognition system

Iris Recognition Using Ridgelets

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