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A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier.

Jaafar H, Ibrahim S, Ramli DA - Comput Intell Neurosci (2015)

Bottom Line: To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented.By removing outliers and reducing the amount of training data, this classifier exhibited faster computation.Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Biometric Group, School of Electrical and Electronic Engineering, Universiti Sains Malaysia Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia.

ABSTRACT
Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.

No MeSH data available.


Overall research architecture.
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Related In: Results  -  Collection


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fig1: Overall research architecture.

Mentions: This paper focuses on solutions for low-quality palm print images and computation times and includes a brief discussion of hand tracking and ROI segmentation. The overall research can be divided into three parts which are the client or smart phone side, internet side, and the server side which are illustrated as in Figure 1.


A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier.

Jaafar H, Ibrahim S, Ramli DA - Comput Intell Neurosci (2015)

Overall research architecture.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4465684&req=5

fig1: Overall research architecture.
Mentions: This paper focuses on solutions for low-quality palm print images and computation times and includes a brief discussion of hand tracking and ROI segmentation. The overall research can be divided into three parts which are the client or smart phone side, internet side, and the server side which are illustrated as in Figure 1.

Bottom Line: To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented.By removing outliers and reducing the amount of training data, this classifier exhibited faster computation.Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Biometric Group, School of Electrical and Electronic Engineering, Universiti Sains Malaysia Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia.

ABSTRACT
Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.

No MeSH data available.