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Proceedings Article

Implications of the advanced mini-max (AMM) classifier on non-cooperative standoff biometrics

[+] Author Affiliations
Kenneth A. Byrd, Mohamed F. Chouikha

Howard Univ. (USA)

Harold Szu

George Washington Univ. (USA) and Army NVESD (USA)

Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 734315 (March 19, 2009); doi:10.1117/12.820832
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From Conference Volume 7343

  • Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII
  • Harold H. Szu; F. Jack Agee
  • Orlando, Florida, USA | April 13, 2009

abstract

AMM classification is an advanced version of the typical nearest neighbor classifier that allows one to minimize interclass dispersion while at the same time, maximizing intraclass separation. A technique based on the simple orthogonal feature space of Pentland eigenfaces, the combination of these two embodiments will become essential components to a non-cooperative standoff biometric system for military, medical and homeland security applications. The incorporation of robotic assistance further pushes the frontiers of possible surveillance and authentication that can be realized with such a system. The ability to perform out of the line of sight (OLS)-based surveillance adds an additional dimension, and thus novelty, to the already expanding methods to acquire and process environment-specific data.

© (2009) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Kenneth A. Byrd ; Harold Szu and Mohamed F. Chouikha
"Implications of the advanced mini-max (AMM) classifier on non-cooperative standoff biometrics", Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 734315 (March 19, 2009); doi:10.1117/12.820832; http://dx.doi.org/10.1117/12.820832


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