Palm Pattern Recognition using Scale Invariant Feature Transform (SIFT)

Kasiselvanathan, M.; Sangeetha, V.; Kalaiselvi, A.

doi:10.1504/ijisc.2018.10023048

IJISC

2018

DOI: 10.1504/ijisc.2018.10023048

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Palm Pattern Recognition using Scale Invariant Feature Transform (SIFT)

M. Kasiselvanathan¹,

V. Sangeetha²,

A. Kalaiselvi³

Abstract: In this research paper, we propose an efficient scale invariant feature transform (SIFT) for palm pattern recognition. A fingerprint recognition which is efficient for individual authentication based on fingerprint pattern. This method leads to fraudulent because it could be extracted easily from individuals. The SIFT method based on feature detection overcomes the above problem and is a combination of fast key point detector and visual descriptor. Using SIFT method contactless palm pattern images can be acqui… Show more

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Cited by 5 publications

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Performance evaluation of SIFT against common image deformations on iban plaited mat motif images

Joseph

Hipiny²,

Ujir³

et al. 2021

IJEECS

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Decorative plaited mat is one of the many examples of rich plait work often seen on Borneo handicraft products. The plaited mats are decorated with simple and complex motif designs; each has its own special meaning and taboos. The motif designs are used as a reflection of environment and the traditional beliefs in the Iban community. In line with efforts from UNESCO’s and Sarawak Government’s, digitization, and the use of IR4.0 technologies to preserve and promote this cultural heritage is encouraged. Towards this end goal, we present a novel image dataset containing 10 Iban plaited mat motif classes. The plaited mat motifs are made of diagonal and symmetrical shapes, as well as geometric and non-geometric patterns. Classification’s accuracy using scale-invariant feature transform (SIFT) features was evaluated against 6 common image deformations: zoom+rotation, viewpoint, image blur, JPEG compression, scale and illumination, across multiple threshold values. Varying degrees of each deformation were applied to a digitally cleaned (and cropped) image of each mat motif class. We used RANSAC to remove outliers from the noisy SIFT matching result. The optimal threshold value is 2.0e-2 with a reported 100.0% matching accuracy for the scale change and zoom+rotation set.

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Performance evaluation of SIFT against common image deformations on iban plaited mat motif images

Joseph

Hipiny²,

Ujir³

et al. 2021

IJEECS

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Optimized Node Clustering based on Received Signal Strength with Particle Ordered-filter Routing Used in VANET

Singh¹,

Kavitha²,

Regin³

et al. 2020

WEB

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VANET is a critical and demanding mission. Numerous methods exist, but none profits in a distributed fashion from physical layer parameters. This paper describes a method that enables individual nodes to estimate node density, independent of beacon messages, and other infrastructure-based information, of their surrounding network. In this paper, a discrete simulator of events was proposed to estimate the average number of simultaneously transmitting nodes, a functional channel model for the VANETs system, and a method of estimating node density. Proposed based on some equations to allow individual nodes to estimate their surrounding node density in real-time Optimized Node Cluster Algorithm with Network Density in which the composition of a cluster is triggered adjacent, these traffic signals is the same and has been predicated mostly on the position a vehicle might well take after crossing. Additional Ordered Tracking with Particle -Filter Routing in which receives simultaneous signal intensity versus node transmission and node density transmission. Conduct multiple location-related analyzes to test the plausibility of the neighboring single-hop nodes on mobility data. The system is designed to operate in the most complex situations where nodes have little knowledge of network topology and the results, therefore, indicate that the system is fairly robust and accurate.

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Review Study on Different Attack Strategies of Worm in a Network

Mondal¹,

Das²,

Nath³

et al. 2020

WEB

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In today’s era Internet worm is a giant threat to the network infrastructure. Although there are different strategies to sense those hazard at early stages. They detect using some signature based approach. But when novel attacks come into the structure, it is very hard to detect them as they do not have any previous signature. For those some signature based methodology is used. In our work we have reviewed different strategies of internet worm detection and prevention and this article also explores the existing techniques to automate signatures for network worms.

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Palm Pattern Recognition using Scale Invariant Feature Transform (SIFT)

Cited by 5 publications

References 1 publication

Performance evaluation of SIFT against common image deformations on iban plaited mat motif images

Performance evaluation of SIFT against common image deformations on iban plaited mat motif images

Optimized Node Clustering based on Received Signal Strength with Particle Ordered-filter Routing Used in VANET

Review Study on Different Attack Strategies of Worm in a Network

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