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

Kenneth A. Byrd; Harold Szu; Mohamed F. Chouikha

doi:10.1117/12.820832

19 March 2009 Implications of the advanced mini-max (AMM) classifier on non-cooperative standoff biometrics

Kenneth A. Byrd, Harold Szu, Mohamed F. Chouikha

Proceedings Volume 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII; 734315 (2009) https://doi.org/10.1117/12.820832
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download 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 (19 March 2009); https://doi.org/10.1117/12.820832

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KEYWORDS

Biometrics

Sensors

Surveillance

Data acquisition

Databases

Facial recognition systems

Robotics

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