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Proceedings Paper

Modular biometric system
Author(s): Charles Hsu; Michael Viazanko; Jimmy O'Looney; Harold Szu
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Paper Abstract

Modularity Biometric System (MBS) is an approach to support AiTR of the cooperated and/or non-cooperated standoff biometric in an area persistent surveillance. Advanced active and passive EOIR and RF sensor suite is not considered here. Neither will we consider the ROC, PD vs. FAR, versus the standoff POT in this paper. Our goal is to catch the "most wanted (MW)" two dozens, separately furthermore ad hoc woman MW class from man MW class, given their archrivals sparse front face data basis, by means of various new instantaneous input called probing faces. We present an advanced algorithm: mini-Max classifier, a sparse sample realization of Cramer-Rao Fisher bound of the Maximum Likelihood classifier that minimize the dispersions among the same woman classes and maximize the separation among different man-woman classes, based on the simple feature space of MIT Petland eigen-faces. The original aspect consists of a modular structured design approach at the system-level with multi-level architectures, multiple computing paradigms, and adaptable/evolvable techniques to allow for achieving a scalable structure in terms of biometric algorithms, identification quality, sensors, database complexity, database integration, and component heterogenity. MBS consist of a number of biometric technologies including fingerprints, vein maps, voice and face recognitions with innovative DSP algorithm, and their hardware implementations such as using Field Programmable Gate arrays (FPGAs). Biometric technologies and the composed modularity biometric system are significant for governmental agencies, enterprises, banks and all other organizations to protect people or control access to critical resources.

Paper Details

Date Published: 19 March 2009
PDF: 14 pages
Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 734317 (19 March 2009); doi: 10.1117/12.820297
Show Author Affiliations
Charles Hsu, Trident Systems Inc. (United States)
Michael Viazanko, Trident Systems Inc. (United States)
Jimmy O'Looney, Trident Systems Inc. (United States)
Harold Szu, George Washington Univ. (United States)


Published in SPIE Proceedings Vol. 7343:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII
Harold H. Szu; F. Jack Agee, Editor(s)

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