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Journal of Electronic Imaging

Use of synthetic data to test biometric algorithms
Author(s): Thomas M. Murphy; Randy Broussard; Ryan Rakvic; Hau Ngo; Robert W. Ives; Robert Schultz; Joseph T. Aguayo
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Paper Abstract

For digital imagery, face detection and identification are functions of great importance in wide-ranging applications, including full facial recognition systems. The development and evaluation of unique and existing face detection and face identification applications require a significant amount of data. Increased availability of such data volumes could benefit the formulation and advancement of many biometric algorithms. Here, the utility of using synthetically generated face data to evaluate facial biometry methodologies to a precision that would be unrealistic for a parametrically uncontrolled dataset, is demonstrated. Particular attention is given to similarity metrics, symmetry within and between recognition algorithms, discriminatory power and optimality of pan and/or tilt in reference images or libraries, susceptibilities to variations, identification confidence, meaningful identification mislabelings, sensitivity, specificity, and threshold values. The face identification results, in particular, could be generalized to address shortcomings in various applications and help to inform the design of future strategies.

Paper Details

Date Published: 8 August 2016
PDF: 11 pages
J. Electron. Imag. 25(4) 043023 doi: 10.1117/1.JEI.25.4.043023
Published in: Journal of Electronic Imaging Volume 25, Issue 4
Show Author Affiliations
Thomas M. Murphy, U.S. Naval Academy (United States)
Randy Broussard, U.S. Naval Academy (United States)
Ryan Rakvic, U.S. Naval Academy (United States)
Hau Ngo, U.S. Naval Academy (United States)
Robert W. Ives, U.S. Naval Academy (United States)
Robert Schultz, U.S. Naval Academy (United States)
Joseph T. Aguayo, North Carolina State Univ. (United States)

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