Use of synthetic data to test biometric algorithms

Thomas M. Murphy; Randy Broussard; Ryan Rakvic; Hau Ngo; Robert W. Ives; Robert Schultz; Joseph T. Aguayo

doi:10.1117/1.JEI.25.4.043023

8 August 2016 Use of synthetic data to test biometric algorithms

Thomas M. Murphy, Randy Broussard, Ryan Rakvic, Hau Ngo, Robert W. Ives, Robert Schultz, Joseph T. Aguayo

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 4, 043023 (August 2016). https://doi.org/10.1117/1.JEI.25.4.043023

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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Thomas M. Murphy, Randy Broussard, Ryan Rakvic, Hau Ngo, Robert W. Ives, Robert Schultz, and Joseph T. Aguayo "Use of synthetic data to test biometric algorithms," Journal of Electronic Imaging 25(4), 043023 (8 August 2016). https://doi.org/10.1117/1.JEI.25.4.043023

Published: 8 August 2016

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