Iris Indexing Techniques: A Review

Poonguzhali, N.; Ezhilarasan, M.

doi:10.5120/12842-0171

Cited by 4 publications

(5 citation statements)

References 26 publications

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“…We used 450 images (225 ocular images of the left eye and 225 ocular images of the right eye, respectively) of 320x240 pixels size. These images belong to the Multimedia University database (MMU1) 1 , which is a public eye image database for iris-based biometrics training models. The experiment was performed in MATLAB R2018a (the MathWorks, Natick, MA, USA).…”

Section: Databasementioning

confidence: 99%

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Iris-Based Biometric Identification Using a Combination of the Right - Left Iris Statistical Features

Dincă Lăzărescu,

Moldovanu,

Moraru

2024

J. Phys.: Conf. Ser.

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A combination between the information extracted for both right iris and left iris could increase the efficacy of the biometric recognition systems. In this paper, we propose a biometric identification method based on density of image patterns extracted from human iris images and the combination and comparison of the right iris and the left iris characteristics. The density of the patters approach for processed images can be a new biometric feature used to implement a biometric recognition system with high performance when a small feature dimension is used. In this way, we can maximize the retention of the effective biometric information. The experiments were conducted on the MMU Iris Database containing 225 images of the left eye and 225 images of the right eye. Two morphological Top-hat and Hit or Miss transforms were implemented to find out the particular pattern of pixels. They allow for the enhancement of detail in images. Then, a statistical feature extraction technique is employed to derive the density of the patterns in morphological transformed images. To assess the density of the patterns differences between the right and left iris data groups, the Pearson’s correlation coefficient (PCC) is computed. We reported very good results with a PCC of 0.6164 (strong and positive correlation) for Top-hat morphological operation whilst the Hit or Miss transform returns a PCC of 0.0127 so there is no relationship between the density of the patterns in the right and left irises.

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Section: Databasementioning

confidence: 99%

“…The textural characteristics of the iris provide unique high-density information that explains why iris recognition is considered the most reliable and accurate biometric identification system available [1]. Omran et al [2] presented an efficient structure for the iris recognition system called IRISNet.…”

Section: Introductionmentioning

confidence: 99%

Iris-Based Biometric Identification Using a Combination of the Right - Left Iris Statistical Features

Dincă Lăzărescu,

Moldovanu,

Moraru

2024

J. Phys.: Conf. Ser.

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“…This is because the codes (templates) generated for comparison are multidimensional data, and methods for specific access domain are needed. Related to the last point, the implementation of the extension is intended to include indexing methods based on IrisCode [7,8,21], in order to allow simple and efficient data management, even for large volumes of data.…”

Section: Preparation Of Manuscriptmentioning

confidence: 99%

“…Since biometrics-based identification systems, especially iris, work with high dimensional characteristics; extensive searching in a large database increases response time. One strategy to improve this aspect is to use indexing techniques [8], as this reduces the search space for an identification system by quickly choosing a subset of iris images from the database in order to determine a possible match. In this sense, it is important for the extension to provide domain indexes associated with identification methods.…”

Section: Introductionmentioning

confidence: 99%

Efficient Iris Recognition Management in Object-Related Databases

Alvez

Miranda

Etchart

et al. 2018

JC&ST

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Biometric applications have grown significantly in recent years, particularly iris-based systems. In the present work, an extension of an Object Relational Database Management System for the integral management of a biometric system based on the human iris was presented. Although at present, there are many database extensions for different domains, in no case for biometric applications. The proposed extension includes both the extension of the type system and the definition of domain indexes for performance improvement. The aim of this work is to provide a tool that facilitates the development of biometric applications based on the iris feature. Its development is based on a reference architecture that includes both the management of images of the iris trait, its associated metadata and the necessary methods for both manipulation and queries. An implementation of the extension is performed for PostgreSQL DBMS, and SP-GiST framework is used in the implementation of a domain index. Experiments were carried out to evaluate the performance of the proposed index, which shows improvements in query execution times.

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“…The iris is a thin circular ring-shaped region positioned between the black pupil and the white sclera of the human eye. It is responsible for the amount of light reaching the retina by controlling the diameter of the pupil [1]. Essentially it consists of randomly generated characteristics that results in very complex and temporally constant patterns.…”

Section: Introductionmentioning

confidence: 99%

Efficient, Accurate, and Rotation-Invariant Iris Code

Damer

Terhörst

Braun

et al. 2017

IEEE Signal Process. Lett.

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The large-scale of the recently demanded biometric systems has put a pressure on creating a more efficient, accurate, and private biometric solutions. Iris biometrics is one of the most distinctive and widely used biometric characteristics. High performing iris representations suffer from the curse of rotation inconsistency. This is usually solved by assuming a range of rotational errors and performing a number of comparisons over this range, which results in a high computational effort and limits indexing and template protection. This work presents a generic and parameter-free transformation of binary iris representation into a rotation-invariant space. The goal is to perform accurate and efficient comparison and enable further indexing and template protection deployment. The proposed approach was tested on a database of 10,000 subjects of the ISYN1 iris database generated by CASIA. Besides providing a compact and rotational-invariant representation, the proposed approach reduced the equal error rate by more than 55% and the computational time by a factor of up to 44 compared to the original representation.

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Iris Indexing Techniques: A Review

Cited by 4 publications

References 26 publications

Iris-Based Biometric Identification Using a Combination of the Right - Left Iris Statistical Features

Iris-Based Biometric Identification Using a Combination of the Right - Left Iris Statistical Features

Efficient Iris Recognition Management in Object-Related Databases

Efficient, Accurate, and Rotation-Invariant Iris Code

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