IRIS TECHNOLOGY: A REVIEW ON IRIS BASED BIOMETRIC SYSTEMS FOR UNIQUE HUMAN IDENTIFICATION

Biometric features are widely used in real time applications for unique human identification. Iris is one of the physiological biometric features which are regarded as highly reliable in biometric identification systems. Often iris is combined with other biometric features for robust biometric systems. It is also observed that biometrics is combined with cryptography for stronger security mechanisms. Since iris is unique for all individuals across the globe, many researchers focused on using iris or along with other biometrics for security with great precision. Multimodal biometric systems came into existence for better accuracy in human authentication. However, iris is considered to be most discriminatory of facial biometrics. Study of iris based human identification in ideal and non-cooperative environments can provide great insights which can help researchers and organizations that depend on iris-based biometric systems. The technical knowhow of iris strengths and weaknesses can be great advantage. This is more important in the wake of widespread use of smart devices which are vulnerable to attacks. This paper throws light into various iris-based biometric systems, issues with iris in the context of texture comparison, cancellable biometrics, iris in multi-model biometric systems, iris localization issues, challenging scenarios pertaining to accurate iris recognition and so on.

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Biometrics in cyber defense

MATEC Web of Conferences ◽

10.1051/matecconf/202030902003 ◽

2020 ◽

Vol 309 ◽

pp. 02003

Author(s):

Gabriela Mogos

Keyword(s):

Literature Review ◽

Iris Recognition ◽

User Authentication ◽

Smart Devices ◽

Biometric Identification ◽

Cyber Defense ◽

Security Risks ◽

Strong Case ◽

Current Practices

Biometric identification is an up and coming authentication method. The growing complexity of and overlap between smart devices, usability patterns and security risks make a strong case for securer and safer user authentication. This paper aims to offer a broad literature review on iris recognition and biometric cryptography to better understand current practices, propose possible future enhancements and anticipate possible future usability and security developments.

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CNN-based multimodal touchless biometric recognition system using Gait and speech

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189765 ◽

2021 ◽

pp. 1-10

Author(s):

Sumit Sarin ◽

Antriksh Mittal ◽

Anirudh Chugh ◽

Smriti Srivastava

Keyword(s):

Neural Networks ◽

Weighted Average ◽

Recognition System ◽

Biometric Recognition ◽

Person Identification ◽

Learning Techniques ◽

Biometric Systems ◽

Multimodal Biometric Systems ◽

Biometric Features ◽

Identification Model

Person identification using biometric features is an effective method for recognizing and authenticating the identity of a person. Multimodal biometric systems combine different biometric modalities in order to make better predictions as well as for achieving increased robustness. This paper proposes a touchless multimodal person identification model using deep learning techniques by combining the gait and speech modalities. Separate pipelines for both the modalities were developed using Convolutional Neural Networks. The paper also explores various fusion strategies for combining the two pipelines and shows how various metrics get affected with different fusion strategies. Results show that weighted average and product fusion rules work best for the data used in the experiments.

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Review and Applications of Multimodal Biometrics for Secured Systems

Advanced Image Processing Techniques and Applications - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2053-5.ch012 ◽

2017 ◽

pp. 251-266

Author(s):

Chitra Anil Dhawale

Keyword(s):

Access Control ◽

Biometric Identification ◽

Control Methods ◽

Biometric Recognition ◽

Multimodal Biometrics ◽

Multimodal Systems ◽

Average Accuracy ◽

Biometric Systems ◽

Electronic Access ◽

Multimodal Biometric Systems

Biometric Systems provide improved security over traditional electronic access control methods such as RFID tags, electronic keypads and some mechanical locks. The user's authorized card or password pin can be lost or stolen. In order for the biometrics to be ultra-secure and to provide more-than-average accuracy, more than one form of biometric identification is required. Hence the need arises for the use of multimodal biometrics. This uses a combination of different biometric recognition technologies. This chapter begins with the basic idea of Biometrics, Biometrics System with its components, Working and proceeds with the need of Multimodal Biometrics with the emphasis on review of various multimodal systems based on fusion ways and fusion level of various features. The last section of this chapter describes various multimodal Biometric Systems.

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An Enhanced IRIS Output Technique (EIOT) for Biometric Security

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b7575.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 4788-4791

Keyword(s):

Iris Recognition ◽

Human Identification ◽

Identification System ◽

Security Systems ◽

Security Applications ◽

Biometric Security ◽

Reliable Systems ◽

Human Eyes ◽

Biometric Features

Human biometric features form the base for many security applications which identify humans uniquely. Human eyes and specifically the Iris based identifications are regarded as highly reliable systems. Iris based systems when combined with cryptography have been able to present higher biometric based security systems. This paper presents an Iris based human identification system called EIOT which can enhance biometric security. It is a set of unique sequential steps followed in Iris recognition and can be implemented in human authentications and identifications.

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Multimodal Biometric Authentication for Smartphones

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39569 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1559-1568

Author(s):

Mrunal Pathak

Keyword(s):

Multimodal Fusion ◽

Biometric Identification ◽

Identification System ◽

Sensitive Information ◽

Biometric Authentication ◽

Biometric System ◽

Multimodal Biometric System ◽

Biometric Systems ◽

Real Time Applications ◽

Eye Recognition

Abstract: Smartphones have become a crucial way of storing sensitive information; therefore, the user's privacy needs to be highly secured. This can be accomplished by employing the most reliable and accurate biometric identification system available currently which is, Eye recognition. However, the unimodal eye biometric system is not able to qualify the level of acceptability, speed, and reliability needed. There are other limitations such as constrained authentication in real time applications due to noise in sensed data, spoof attacks, data quality, lack of distinctiveness, restricted amount of freedom, lack of universality and other factors. Therefore, multimodal biometric systems have come into existence in order to increase security as well as to achieve better performance.[1] This paper provides an overview of different multimodal biometric (multibiometric) systems for smartphones being employed till now and also proposes a multimodal biometric system which can possibly overcome the limitations of the current biometric systems. Keywords: Biometrics, Unimodal, Multimodal, Fusion, Multibiometric Systems

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A Survey on Unimodal, Multimodal Biometrics and Its Fusion Techniques

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.36.24224 ◽

2018 ◽

Vol 7 (4.36) ◽

pp. 689 ◽

Cited By ~ 1

Author(s):

A. S. Raju ◽

V. Udayashankara

Keyword(s):

Information Fusion ◽

Human Identification ◽

Future Research ◽

Biometric System ◽

Multimodal Biometrics ◽

Multimodal Biometric System ◽

Face Features ◽

Biometric Systems ◽

Characteristic Sequences ◽

Multimodal Biometric Systems

Presently, a variety of biometric modalities are applied to perform human identification or user verification. Unimodal biometric systems (UBS) is a technique which guarantees authentication information by processing distinctive characteristic sequences and these are fetched out from individuals. However, the performance of unimodal biometric systems restricted in terms of susceptibility to spoof attacks, non-universality, large intra-user variations, and noise in sensed data. The Multimodal biometric systems defeat various limitations of unimodal biometric systems as the sources of different biometrics typically compensate for the inherent limitations of one another. The objective of this article is to analyze various methods of information fusion for biometrics, and summarize, to conclude with direction on future research proficiency in a multimodal biometric system using ECG, Fingerprint and Face features. This paper is furnished as a ready reckoner for those researchers, who wish to persue their work in the area of biometrics.

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Age dependency of the diabetes effects on the iris recognition systems performance evaluation results

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2019-0246 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Mohammadreza Azimi ◽

Seyed Ahmad Rasoulinejad ◽

Andrzej Pacut

Keyword(s):

Iris Recognition ◽

Recognition Task ◽

Aging Effect ◽

Recognition System ◽

Health Condition ◽

Age Dependency ◽

Biometric Systems ◽

Kolmogorov Smirnov ◽

Recognition Systems ◽

Smirnov Test

AbstractIn this paper, we attempt to answer the questions whether iris recognition task under the influence of diabetes would be more difficult and whether the effects of diabetes and individuals’ age are uncorrelated. We hypothesized that the health condition of volunteers plays an important role in the performance of the iris recognition system. To confirm the obtained results, we reported the distribution of usable area in each subgroup to have a more comprehensive analysis of diabetes effects. There is no conducted study to investigate for which age group (young or old) the diabetes effect is more acute on the biometric results. For this purpose, we created a new database containing 1,906 samples from 509 eyes. We applied the weighted adaptive Hough ellipsopolar transform technique and contrast-adjusted Hough transform for segmentation of iris texture, along with three different encoding algorithms. To test the hypothesis related to physiological aging effect, Welches’s t-test and Kolmogorov–Smirnov test have been used to study the age-dependency of diabetes mellitus influence on the reliability of our chosen iris recognition system. Our results give some general hints related to age effect on performance of biometric systems for people with diabetes.

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