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

Duaa AlSaeed, Ahmed Bouridane, Ali ElZaart, Rachid Sammouda

Research output: Contribution to conferencePaperpeer-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 languageEnglish
DOIs
Publication statusPublished - Jul 2012
EventIET Conference on Image Processing (IPR 2012) - University of Westminster, London
Duration: 1 Jul 2012 → …

Conference

ConferenceIET Conference on Image Processing (IPR 2012)
Period1/07/12 → …

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