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

AlSaeed, Duaa, Bouridane, Ahmed, ElZaart, Ali and Sammouda, Rachid (2012) Novel fingerprint segmentation with entropy-Li MCET using log-normal distribution. In: IET Conference on Image Processing (IPR 2012), 3-4 July 2012, University of Westminster, London.

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Ellen Cole
Date Deposited: 19 Dec 2012 11:31
Last Modified: 13 Oct 2019 00:32

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