IWT Based Iris Recognition for Image Authentication

Singh, Ghanapriya; Singh, Ratnesh Kumar; Saha, Rajesh; Agarwal, Nidhi

doi:10.1016/j.procs.2020.04.200

Cited by 34 publications

(15 citation statements)

References 17 publications

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“…So, the sensitive user's biological information can be safe to some extent. (Singh et al, 2020) use machine learning in iris recognition. (Bodade et al, 2009) introduces a technique for detecting the inner iris border based on differences in eye pupil size, where the eye pupil size can change according to the light conditions.…”

Section: Iris Recognitionmentioning

confidence: 99%

“…Recently some researchers have been presented a survey about using neural networks in BAS to recognize biometric features of a person like a face (Boulgouris et al, 2020), iris (Singh et al, 2020), voice (Patel et al, 2018), signature and fingerprint (Yang et al, 2018), etc. (Kumar, 2016) has been studied the limitations of unimodal BAS and presented a mmultimodal BAS such as the combination of the face with fingerprint traits for a more secure authentication system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Smartphone Protecting Identification Against Attacks Using Biometric Authentication Systems

Said

Mohamed

Elshenawy

et al. 2021

Journal of Al-Azhar University Engineering Sector

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Recently, a lot of Biometric Authentication Systems (BAS) techniques for the purpose of security in both industrial and academic organizations because it improves the authentication system's usability by overcoming the existing difficulties of using passwords. Although of the rapid spread of BAS's systems especially in smartphones, it still has defects and are subjected to attacks, and suffers from the intrusion of user privacy, because some biometric features have not been deeply studied and investigated. In this survey, the existing BAS techniques are explored and classified focusing on the security issues that they are used for and the corresponding solutions provided in this domain. The advantages and disadvantages of each BAS technique are also reviewed, the threats of each BAS technique are listed, summarized, and discussed. The relevant research studies are also presented and several open problems, research direction are also presented.

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Section: Iris Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Smartphone Protecting Identification Against Attacks Using Biometric Authentication Systems

Said

Mohamed

Elshenawy

et al. 2021

Journal of Al-Azhar University Engineering Sector

View full text Add to dashboard Cite

show abstract

“…They tested their model against the CASIA-Iris-Thousand dataset and get a 90% accuracy of recognition rate, which is considered a relatively moderate recognition rate due to using a pre-trained model and the loss of biometric information during training. An iris recognition system is proposed in Singh et al (2020), where the researchers used Integer Wavelet Transform (IWT) as an iris feature extractor and normalized Hamming distance as a classifier. The UBIRIS.v2 dataset was used for testing their model.…”

Section: Related Workmentioning

confidence: 99%

An efficient multi-factor authentication scheme based CNNs for securing ATMs over cognitive-IoT

Shalaby¹,

Gad²,

Hemdan³

et al. 2021

PeerJ Computer Science

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Nowadays, the identity verification of banks’ clients at Automatic Teller Machines (ATMs) is a very critical task. Clients’ money, data, and crucial information need to be highly protected. The classical ATM verification method using a combination of credit card and password has a lot of drawbacks like Burglary, robbery, expiration, and even sudden loss. Recently, iris-based security plays a vital role in the success of the Cognitive Internet of Things (C-IoT)-based security framework. The iris biometric eliminates many security issues, especially in smart IoT-based applications, principally ATMs. However, integrating an efficient iris recognition system in critical IoT environments like ATMs may involve many complex scenarios. To address these issues, this article proposes a novel efficient full authentication system for ATMs based on a bank’s mobile application and a visible light environments-based iris recognition. It uses the deep Convolutional Neural Network (CNN) as a feature extractor, and a fully connected neural network (FCNN)—with Softmax layer—as a classifier. Chaotic encryption is also used to increase the security of iris template transmission over the internet. The study and evaluation of the effects of several kinds of noisy iris images, due to noise interference related to sensing IoT devices, bad acquisition of iris images by ATMs, and any other system attacks. Experimental results show highly competitive and satisfying results regards to accuracy of recognition rate and training time. The model has a low degradation of recognition accuracy rates in the case of using noisy iris images. Moreover, the proposed methodology has a relatively low training time, which is a useful parameter in a lot of critical IoT based applications, especially ATMs in banking systems.

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“…Singh, Singh, Saha, and Agarwal (2020) employed Integer Wavelet Transform (IWT) features for iris recognition. They claimed 98.9% accuracy while comparing with iris recognition using discrete wavelet transform.…”

Section: Current State Of the Artmentioning

confidence: 99%

Light microscopic iris classification using ensemble multi‐class support vector machine

Rehman

2021

Microscopy Res & Technique

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Similar to other biometric systems such as fingerprint, face, DNA, iris classification could assist law enforcement agencies in identifying humans. Iris classification technology helps law‐enforcement agencies to recognize humans by matching their iris with iris data sets. However, iris classification is challenging in the real environment due to its invertible and complex texture variations in the human iris. Accordingly, this article presents an improved Oriented FAST and Rotated BRIEF with Bag‐of‐Words model to extract distinct and robust features from the iris image, followed by ensemble multi‐class‐SVM to classify iris. The proposed methodology consists of four main steps; first, iris image normalization and enhancement; second, localizing iris region; third, iris feature extraction; finally, iris classification using ensemble multi‐class support vector machine. For preprocessing of input images, histogram equalization, Gaussian mask and median filters are applied. The proposed technique is tested on two benchmark databases, that is, CASIA‐v1 and iris image database, and achieved higher accuracy than other existing techniques reported in state of the art.

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IWT Based Iris Recognition for Image Authentication

Cited by 34 publications

References 17 publications

A Survey on Smartphone Protecting Identification Against Attacks Using Biometric Authentication Systems

A Survey on Smartphone Protecting Identification Against Attacks Using Biometric Authentication Systems

An efficient multi-factor authentication scheme based CNNs for securing ATMs over cognitive-IoT

Light microscopic iris classification using ensemble multi‐class support vector machine

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