Fingerprint classification using Support Vector Machine

Alias, Nurul Ain; Radzi, Nor Haizan Mohamed

doi:10.1109/ict-ispc.2016.7519247

Cited by 21 publications

(10 citation statements)

References 4 publications

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“…The two experiments were run 12 times for the 12 FVCs databases. The results in figures (11)(12)(13) show that, the FMR and FNMR of spectrum based verification approach are less than the FMR and FNMR of minutiae based verification approach. These results The results ensure the fidelity of the spectrum based verification approach to the minutiae based verification approach and the other published results over the different FVCs databases.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The support vector machine is a learning machine used for two groups classification problems, it has been used as a classifier for fingerprint matching by many researchers [13][14], SVM has shown outstanding classification performance in practice as classifier [39,40]. SVM can be used for two class classification problems where the data can be separated by a hyperplane defined by a number of support vectors.…”

Section: Figure (5): Block Diagram Of Minutiae Based Fingerprint Verimentioning

confidence: 99%

“…Although many approaches have been developed for improving the performance of the fingerprint verification technique, the extraction of the fingerprints and the verification accuracy still main challenges face this technique. Support Vector Machines (SVMs) have been used as a classifier for fingerprint verification by many researchers [13][14][15], others used neural networks [16][17]. In this paper SVMs have been used for features matching in the proposed approach.…”

Section: Introductionmentioning

confidence: 99%

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Fingerprints verification based on their spectrum

Kasban

2016

Neurocomputing

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Figure (5): Block Diagram Of Minutiae Based Fingerprint Verimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fingerprints verification based on their spectrum

Kasban

2016

Neurocomputing

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“…It maximizes the gap between the decision boundary and training patterns. Authors of [184] have discussed the use of SVM in digital fingerprinting in detail. They have also compared it with other traditional models.…”

Section: Digital Fingerprintingmentioning

confidence: 99%

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

et al. 2019

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The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an evergrowing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed. INDEX TERMS Internet of Things (IoT), IoT security, blockchain, fog computing, edge computing, machine learning, IoT applications, distributed systems.

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“…Alias and Radzi [10] have exhibited SVM calculation utilised for creating fingerprint grouping model. This research has presented fingerprint data collection acquired from fingerprint verification competition (FVC) 2000 and FVC 2002.…”

Section: Related Workmentioning

confidence: 99%

FPR using machine learning with multi‐feature method

Kumar

Singh

2018

IET image process

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Biometrics authentication is considered as most secure and reliable method to recognise and identify person's identity. Researchers put efforts to find efficient ways to secure and classify the solutions to biometric problems. In this category, fingerprint recognition (FPR) is most widely used biometric trait for person identification/verification. The present work focuses an FPR technique, which uses the grey‐level difference method, discrete wavelet transforms and edge histogram descriptor for fingerprint representation and matching. Wavelet shrinkage used for noise removal in the image. Ridge flow estimation is calculated using the gradient approach. SVM and Hamming distance similarity measures are used for recognition. The experiment result has been tested on the standard 2000–2004 fingerprint verification competition dataset and the accuracy of proposed algorithm was reported to be well above 98%.

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Fingerprint classification using Support Vector Machine

Cited by 21 publications

References 4 publications

Fingerprints verification based on their spectrum

Fingerprints verification based on their spectrum

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

FPR using machine learning with multi‐feature method

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