A survey of iris datasets

Omelina, Luboš; Goga, Jozef; Pavlovičová, Jarmila; Oravec, Milos; Jansen, Bart

doi:10.1016/j.imavis.2021.104109

Cited by 39 publications

(20 citation statements)

References 49 publications

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“…The datasets collected by the respective authors for their study and model training are called custom/real-time datasets. This study exclusively stresses standard benchmark datasets as the appropriate information related to custom real-time datasets is inaccessible [70].…”

Section: Iris Datasets (Rq3)mentioning

confidence: 99%

“…Since 2003, the updated versions of the CASIA dataset are available. The updated versions of the CASIA dataset have made it the most widely used standard in the analysis process of iris recognition methods [70].…”

Section: Standard Benchmark Datasetsmentioning

confidence: 99%

“…The most popular datasets in the uncontrolled environment are UBIRIS-V1 [72], UBIRIS-V2 [72], and UPOL [73]. These datasets together serve as a benchmark and are referred by the majority of authors [70]. The UPOL datasets contain the high-quality of the 64 people's iris images were taken via an uncontrolled "environment.…”

Section: B Uncontrolled Environment/visible Light Datasetsmentioning

confidence: 99%

“…The UPOL datasets contain the high-quality of the 64 people's iris images were taken via an uncontrolled "environment. The UBIRIS datasets contain the noisy images captured" in an uncontrolled/visible light "environment, in the case of UBIRIS-V2", iris images are captured at a distance and are on the move [70].…”

Section: B Uncontrolled Environment/visible Light Datasetsmentioning

confidence: 99%

“…Table 8 displays all iris datasets listing, rows Nos. 1 to 30 represent the controlled environment datasets [70]. In iris liveness detection, the images captured using a controlled environment are less frequently used [58] than those captured in visible light/uncontrolled environment.…”

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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: Iris Datasets (Rq3)mentioning

confidence: 99%

Section: Standard Benchmark Datasetsmentioning

confidence: 99%