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Use of synthetic data to test biometric algorithms

[+] Author Affiliations
Thomas M. Murphy, Ryan Rakvic, Hau Ngo, Robert W. Ives, Robert Schultz

United States Naval Academy, Center for Biometric Signal Processing, Department of Electrical and Computer Engineering, 105 Maryland Avenue, Annapolis, Maryland 21402, United States

Randy Broussard

United States Naval Academy, Center for Biometric Signal Processing, Department of Electrical and Computer Engineering, 105 Maryland Avenue, Annapolis, Maryland 21402, United States

United States Naval Academy, Center for Biometric Signal Processing, Department of Weapons and Systems Engineering, 105 Maryland Avenue, Annapolis, Maryland 21401, United States

Joseph T. Aguayo

North Carolina State University, Laboratory for Analytic Sciences, Raleigh, North Carolina 27695, United States

J. Electron. Imaging. 25(4), 043023 (Aug 08, 2016). doi:10.1117/1.JEI.25.4.043023
History: Received February 29, 2016; Accepted July 14, 2016
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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.

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© 2016 SPIE and IS&T

Citation

Thomas M. Murphy ; Randy Broussard ; Ryan Rakvic ; Hau Ngo ; Robert W. Ives, et al.
"Use of synthetic data to test biometric algorithms", J. Electron. Imaging. 25(4), 043023 (Aug 08, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.4.043023


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