Back to articles
Articles
Volume: 28 | Article ID: art00016
Image
Comparison Study of Gaussian Mixture Models for Fingerprint Image Duplication with a New Model
  DOI :  10.2352/ISSN.2470-1173.2016.15.IPAS-183  Published OnlineFebruary 2016
Abstract

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.

Subject Areas :
Views 2
Downloads 0
 articleview.views 2
 articleview.downloads 0
  Cite this article 

Sos S Agaian, Rushikesh D Yeole, Gary Reinecke, . Mary-Ann, Mike Troy, "Comparison Study of Gaussian Mixture Models for Fingerprint Image Duplication with a New Modelin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XIV,  2016,  https://doi.org/10.2352/ISSN.2470-1173.2016.15.IPAS-183

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2016
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA