Cross-sensor iris spoofing detection using orthogonal features

Kaur, Bineet; Singh, Sukhwinder; Kumar, Jagdish

doi:10.1016/j.compeleceng.2018.12.002

Cited by 18 publications

(19 citation statements)

References 14 publications

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“…Several studies [10,21,24,44,50,51,60] have confirmed the necessity for sensing contact lenses. The transparent (Clear) and textured (cosmetic) lenses have been revealed to affect the iris recognition systems.…”

Section: Contact Lenses Attacksmentioning

confidence: 93%

“…Iris has complex textures and unique features, so it is widely used in identifying and authentication the person in many applications [9]. Aadhar project uses the biometric system to identify the citizens of India, Amsterdam airport, and the US Canadian border [10]. Even though the iris has a unique texture pattern, there is a possibility of spoofing by the imposter.…”

Section: Hospitality and Tourismmentioning

confidence: 99%

“…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%

“…The SVM classifier gives the best Spoof Detection accuracy for the classification of Live and Print attack images. After SVM [10], Random Forest and Decision Tree classifiers give good classification accuracy. In [22], Fathy proves that Wavelet Packets (WPs) and LBP with SVM as the classifiers give 99.92% Print image detection accuracy.…”

Section: Print 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: Contact Lenses Attacksmentioning

confidence: 93%

Section: Hospitality and Tourismmentioning

confidence: 99%

Section: Print Attacksmentioning

confidence: 99%

Section: Print 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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“…Likewise, the rubber-sheet model was utilized by Bakshi et al [72], Djoumessi [75], Gad et al [80], Salve and Narote [132], Nguyen et al [158], and Kaur et al [210] to normalize the iris region.…”

Section: Normalization Techniquesmentioning

confidence: 99%

Iris Recognition Development Techniques: A Comprehensive Review

2021

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Recently, iris recognition techniques have achieved great performance in identification. Among authentication techniques, iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase. The recognition system is divided into seven phases, namely, the acquisition phase in which the iris images are acquired, the preprocessing phase in which the quality of the iris image is improved, the segmentation phase in which the iris region is separated from the background of the image, the normalization phase in which the segmented iris region is shaped into a rectangle, the feature extraction phase in which the features of the iris region are extracted, the feature selection phase in which the unique features of the iris are selected using feature selection techniques, and finally the classification phase in which the iris images are classified. This article also explains the two approaches of iris recognition which are the traditional approach and the deep learning approach. In addition, the researchers discuss the advantages and disadvantages of previous techniques as well as the limitations and benefits of both the traditional and deep learning approaches of iris recognition. This study can be considered as an initial step towards a large-scale study about iris recognition.

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On Data-Driven Approaches for Presentation Attack Detection in Iris Recognition Systems

Sharma

Selwal

2021

Lecture Notes in Electrical Engineering

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Cross-sensor iris spoofing detection using orthogonal features

Cited by 18 publications

References 14 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 Recognition Development Techniques: A Comprehensive Review

On Data-Driven Approaches for Presentation Attack Detection in Iris Recognition Systems

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