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Open Access Comparison Study of Gaussian Mixture Models for Fingerprint Image Duplication with a New Model

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This paper presents a comparison study of Gaussian Mixture Models for fingerprints image duplication and analysis. It also presents a new probabilistic Parametric Gaussian Mixture Model(GMM). The system is built around the likelihood ratio test for verification, using simple but effective GMMs for likelihood functions and a form of Bayesian adaptation to derive the models. The Computer simulation show that the developed new algorithms have the most optimal performance as compared to state of art algorithms GMMs, Generalized GMMs, Finite Bayesian learning for GMMS, Texture Synthesis and Improved Adaptive Algorithm. The performance of the presented algorithm was evaluated by Bovik Index, Entropy and Mean Square Error.
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Document Type: Research Article

Publication date: February 14, 2016

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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