Intelligent multimodal identification system based on local feature fusion between iris and finger vein

Abed, Enas Abbas; Mohammed, Rana Jassim; Shihab, Dhafer Taha

doi:10.11591/ijeecs.v21.i1.pp224-232

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…The transfer learning can be used to deal with the nonappearance of a large iris dataset. CNN's that have been trained on other large datasets such as ImageNet [39], can be assumed directly to the iris recognition domain. In detail, CNN models pre-trained on ImageNet, have been effectively shifted to many computers vision tasks [25].…”

Section: B Pre-trained Modelmentioning

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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Section: B Pre-trained Modelmentioning

confidence: 99%

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

et al. 2021

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“…The biometric identification system can be defined as the most flexible and efficient way to identify or authenticate individuals [1], [2], as one of the main advantages of these systems is that the concerned person does not need to remember or write a password or carry a smart card, which leads to faster speed use and reduce password management costs. of the nail.…”

Section: Introductionmentioning

confidence: 99%

A new approach to develop biometric fingerprint using human right thumb fingernail

Al-Sultan

Alhialy

Alkhaled

et al. 2023

IJEECS

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In this article, we analyzed the structure of human nails to develop a modern biometric authentication system based on the nail bed and finger lunula. The results of the studies on the collected images proved that each fingernail has distinctive characteristics in terms of the length and width of the nail and the lunula, even identical twins. We focused on the fingernail of the right thumb because of its large size and the accuracy and clarity of the nail. The mediation of the indicative points on the nail bed and on the lunula was used to form the pentagonal structure and use it as a region of interest. Then an ensemble independent component analysis, principal component analysis, haar wavelet, and scale invariant feature transformation, were used. Later we classified these algorithms using support vector machine and Naive Bayes techniques, the performance of each algorithm was analyzed by feature extraction with two classifiers. This study was conducted on 100 participants and showed that this new method could be used as a biometric identification system for humans. There was no similarity in results for all verified samples.

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“…Many works dealing with fingerprint verification task is present in recent years [2], [3], [6]- [8], [10]- [12], [15], [18], [20], [28], [29], [34], [37]- [39], [41], [44]- [47], [57], [58], [63]- [89]. Kommini et al [63] and Sharma et al [72] presented a method of fingerprint recognition based on fractal features extracted of the separated image.…”

Section: Introductionmentioning

confidence: 99%

User identification system for inked fingerprint pattern based on central moments

Baker

Alazawi

Ahmed

et al. 2021

IJEECS

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The <span>use of the fingerprint recognition has been and remains very important in many security applications and licensing systems. Fingerprint recognition is required in many areas such as licensing access to networks, corporate computers and organizations. In this paper, the system of fingerprint recognition that can be used in several cases of fingerprint such as being rounded at an angle by a randomly inked fingerprint on paper. So, fingerprint image is tooked at a different angle in order to identify the owner of the ink fingerprint. This method involves two working levels. The first one, the fingerprint pattern's shape features are calculated based on the central moments of each image being listed on a regular basis with three states rotation. Each image is rotated at a specified angle. In the second level, the fingerprint holder entered is identified using the previously extracted shape features and compared to the three local databases content of three rotation states. When applied the method for several persons by taken their inked fingerprint on the paper, the accuracy of the system in identifying the owner of the fingerprint after rotation states were close to 83.71.</span>

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Intelligent multimodal identification system based on local feature fusion between iris and finger vein

Cited by 9 publications

References 23 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

A new approach to develop biometric fingerprint using human right thumb fingernail

User identification system for inked fingerprint pattern based on central moments

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