Missing data reconstruction using Gaussian mixture models for fingerprint images

Sos S. Agaian; Rushikesh D. Yeole; Shishir P. Rao; Marzena Mulawka; Mike Troy; Gary Reinecke

doi:10.1117/12.2224381

19 May 2016 Missing data reconstruction using Gaussian mixture models for fingerprint images

Sos S. Agaian, Rushikesh D. Yeole, Shishir P. Rao, Marzena Mulawka, Mike Troy, Gary Reinecke

Proceedings Volume 9869, Mobile Multimedia/Image Processing, Security, and Applications 2016; 986905 (2016) https://doi.org/10.1117/12.2224381
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

Abstract

Publisher’s Note: This paper, originally published on 25 May 2016, was replaced with a revised version on 16 June 2016. If you downloaded the original PDF, but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance. One of the most important areas in biometrics is matching partial fingerprints in fingerprint databases. Recently, significant progress has been made in designing fingerprint identification systems for missing fingerprint information. However, a dependable reconstruction of fingerprint images still remains challenging due to the complexity and the ill-posed nature of the problem. In this article, both binary and gray-level images are reconstructed. This paper also presents a new similarity score to evaluate the performance of the reconstructed binary image. The offered fingerprint image identification system can be automated and extended to numerous other security applications such as postmortem fingerprints, forensic science, investigations, artificial intelligence, robotics, all-access control, and financial security, as well as for the verification of firearm purchasers, driver license applicants, etc.

Citation Download Citation

Sos S. Agaian, Rushikesh D. Yeole, Shishir P. Rao, Marzena Mulawka, Mike Troy, and Gary Reinecke "Missing data reconstruction using Gaussian mixture models for fingerprint images", Proc. SPIE 9869, Mobile Multimedia/Image Processing, Security, and Applications 2016, 986905 (19 May 2016); https://doi.org/10.1117/12.2224381

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KEYWORDS

Reconstruction algorithms

Data modeling

Algorithm development

Genetic algorithms

Computer security

Control systems

Image filtering

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