Presentation attack detection for iris recognition using deep learning

Arora, Shefali; Bhatia, M. P. S.

doi:10.1007/s13198-020-00948-1

Cited by 17 publications

(9 citation statements)

References 30 publications

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“…Many studies focus on the print attacks in literature [10,13,21,22,44,[50][51][52]. While identifying the print attacks, authors used different feature extraction techniques in the literature.…”

Section: Print Attacksmentioning

confidence: 99%

“…Recently in literature, CNN and different versions of CNN were also used to identify the Print at-tacks. Many authors used CNN [13,43,52,[54][55][56][57], for Print attacks identification. The author [52] shows that a Convolutional Neural Network (CNN) gives the best classification accuracy of the Print attacks.…”

Section: Print Attacksmentioning

confidence: 99%

“…Many authors used CNN [13,43,52,[54][55][56][57], for Print attacks identification. The author [52] shows that a Convolutional Neural Network (CNN) gives the best classification accuracy of the Print attacks. Figure 12 shows the sample images of the different types of spoofing attacks.…”

Section: Print Attacksmentioning

confidence: 99%

“…Recently Deep Learning algorithms [11,13,52,57,[61][62][63] are also used for Iris spoofing attack detection. The author [64] proves that using BNCNN with self-learn features gives a 100% correct recognition rate.…”

Section: Contact Lenses Attacksmentioning

confidence: 99%

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Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

et al. 2021

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Biometrics is progressively becoming vital due to vulnerabilities of traditional security systems leading to frequent security breaches. Biometrics is an automated device that studies human beings’ physiological and behavioral features for their unique classification. Iris-based authentication offers stronger, unique, and contactless identification of the user. Iris liveness detection (ILD) confronts challenges such as spoofing attacks with contact lenses, replayed video, and print attacks, etc. Many researchers focus on ILD to guard the biometric system from attack. Hence, it is vital to study the prevailing research explicitly associated with the ILD to address how developing technologies can offer resolutions to lessen the evolving threats. An exhaustive survey of papers on the biometric ILD was performed by searching the most applicable digital libraries. Papers were filtered based on the predefined inclusion and exclusion criteria. Thematic analysis was performed for scrutinizing the data extracted from the selected papers. The exhaustive review now outlines the different feature extraction techniques, classifiers, datasets and presents their critical evaluation. Importantly, the study also discusses the projects, research works for detecting the iris spoofing attacks. The work then realizes in the discovery of the research gaps and challenges in the field of ILD. Many works were restricted to handcrafted methods of feature extraction, which are confronted with bigger feature sizes. The study discloses that dep learning based automated ILD techniques shows higher potential than machine learning techniques. Acquiring an ILD dataset that addresses all the common Iris spoofing attacks is also a need of the time. The survey, thus, opens practical challenges in the field of ILD from data collection to liveness detection and encourage future research.

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Section: Print Attacksmentioning

confidence: 99%

Section: Print Attacksmentioning

confidence: 99%

Section: Print Attacksmentioning

confidence: 99%

Section: Contact Lenses Attacksmentioning

confidence: 99%

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Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

et al. 2021

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“…Especially it is connected with iris-based systems. This problem was deeply described in [7]. In the paper, the authors presented that print attack images of live iris, use of contact lenses and conjunction of both can have huge influence on false-positive recognition by the system.…”

Section: How Others See Itmentioning

confidence: 99%

Iris-based human identity recognition with machine learning methods and discrete fast Fourier transform

Szymkowski

Jasiński

Saeed

2021

Innovations Syst Softw Eng

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One of the most important modules of computer systems is the one that is responsible for user safety. It was proven that simple passwords and logins cannot guarantee high efficiency and are easy to obtain by the hackers. The well-known alternative is identity recognition based on biometrics. In recent years, more interest was observed in iris as a biometrics trait. It was caused due to high efficiency and accuracy guaranteed by this measurable feature. The consequences of such interest are observable in the literature. There are multiple, diversified approaches proposed by different authors. However, neither of them uses discrete fast Fourier transform (DFFT) components to describe iris sample. In this work, the authors present their own approach to iris-based human identity recognition with DFFT components selected with principal component analysis algorithm. For classification, three algorithms were used—k-nearest neighbors, support vector machines and artificial neural networks. Performed tests have shown that satisfactory results can be obtained with the proposed method.

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An Overview of Deep Learning Techniques for Biometric Systems

Almabdy

Elrefaei

2020

Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications

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Presentation attack detection for iris recognition using deep learning

Cited by 17 publications

References 30 publications

Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

Iris-based human identity recognition with machine learning methods and discrete fast Fourier transform

An Overview of Deep Learning Techniques for Biometric Systems

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