Novel fingerprint segmentation with entropy-Li MCET using log-normal distribution

Duaa AlSaeed; Ahmed Bouridane; Ali ElZaart; Rachid Sammouda

doi:10.1049/cp.2012.0455

Novel fingerprint segmentation with entropy-Li MCET using log-normal distribution

Duaa AlSaeed, Ahmed Bouridane, Ali ElZaart, Rachid Sammouda

Computer and Information Sciences Department

Research output: Contribution to conference › Paper › peer-review

Abstract

Fingerprint recognition is an important biometric application. This process consists of several phases including fingerprint segmentation. This paper proposes a new method for fingerprint segmentation using a modified Iterative Minimum Cross Entropy Thresholding (MCET) method. The main idea is to model fingerprint images as a mixture of two Log-normal distributions. The proposed method was applied on bi-modal fingerprint images and promising experimental results were obtained. Evaluation of the resulting segmented fingerprint images shows that the proposed method yields better estimation of the optimal threshold than does the same MCET method with Gamma and Gaussian distributions.

Original language	English
DOIs	https://doi.org/10.1049/cp.2012.0455
Publication status	Published - Jul 2012
Event	IET Conference on Image Processing (IPR 2012) - University of Westminster, London Duration: 1 Jul 2012 → …

Conference

Conference	IET Conference on Image Processing (IPR 2012)
Period	1/07/12 → …

Access to Document

10.1049/cp.2012.0455

Cite this

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title = "Novel fingerprint segmentation with entropy-Li MCET using log-normal distribution",

abstract = "Fingerprint recognition is an important biometric application. This process consists of several phases including fingerprint segmentation. This paper proposes a new method for fingerprint segmentation using a modified Iterative Minimum Cross Entropy Thresholding (MCET) method. The main idea is to model fingerprint images as a mixture of two Log-normal distributions. The proposed method was applied on bi-modal fingerprint images and promising experimental results were obtained. Evaluation of the resulting segmented fingerprint images shows that the proposed method yields better estimation of the optimal threshold than does the same MCET method with Gamma and Gaussian distributions.",

author = "Duaa AlSaeed and Ahmed Bouridane and Ali ElZaart and Rachid Sammouda",

year = "2012",

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AU - Bouridane, Ahmed

AU - ElZaart, Ali

AU - Sammouda, Rachid

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N2 - Fingerprint recognition is an important biometric application. This process consists of several phases including fingerprint segmentation. This paper proposes a new method for fingerprint segmentation using a modified Iterative Minimum Cross Entropy Thresholding (MCET) method. The main idea is to model fingerprint images as a mixture of two Log-normal distributions. The proposed method was applied on bi-modal fingerprint images and promising experimental results were obtained. Evaluation of the resulting segmented fingerprint images shows that the proposed method yields better estimation of the optimal threshold than does the same MCET method with Gamma and Gaussian distributions.

AB - Fingerprint recognition is an important biometric application. This process consists of several phases including fingerprint segmentation. This paper proposes a new method for fingerprint segmentation using a modified Iterative Minimum Cross Entropy Thresholding (MCET) method. The main idea is to model fingerprint images as a mixture of two Log-normal distributions. The proposed method was applied on bi-modal fingerprint images and promising experimental results were obtained. Evaluation of the resulting segmented fingerprint images shows that the proposed method yields better estimation of the optimal threshold than does the same MCET method with Gamma and Gaussian distributions.

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