Classifiers in IRIS Biometrics for Personal Authentication

Current research in biometrics aims to develop high-performance tools, which would make it possible to better extract the traits specific to each individual and to grasp their discriminating characteristics. This research is based on high-level analyses of images, captured from the candidate to identify, for a better understanding and interpretation of these signals. Several biometric identification systems exist. The recognition systems based on the iris have many advantages and they are among the most reliable. In this paper, we propose a new approach based on biometric iris authentication. A new scheme was made in this work that consists of calculating a three-dimensional head pose to capture a good iris image from a video sequence which affects the identification results. From this image, we were able to locate the iris and analyse its texture by intelligent use of Meyer wavelets. Our approach was evaluated and approved through two databases CASIA Iris Distance and MiraclHB. The comparative study showed its effectiveness compared to those in the literature.

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“…Signature part FRR CRR CAR FAR Our approach 0.02 0.9715 0.9795 0.0202 Iglesias et al [42] 0.09 NA NA 0.13 Pradeepa et al [43] 0.0315 NA NA 0.0274…”

Section: Resultsmentioning

confidence: 99%

Three-Dimensional Head-Pose Estimation for Smart Iris Recognition from a Calibrated Camera

Akrout

Fakhfakh

2020

Mathematical Problems in Engineering

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“…ROC graph shows the performance of an iris, the classification model at all classification thresholds. Authors [10,[99][100][101] used TPR, FPR, and ROC for evaluating the performances of the iris detection system.…”

Section: E False Positive Ratementioning

confidence: 99%

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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Classifiers in IRIS Biometrics for Personal Authentication

Cited by 2 publications

References 7 publications

Three-Dimensional Head-Pose Estimation for Smart Iris Recognition from a Calibrated Camera

Three-Dimensional Head-Pose Estimation for Smart Iris Recognition from a Calibrated Camera

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

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