Towards Repeatable, Reproducible, and Efficient Biometric Technology Evaluations

Published

August 18, 2015

Author(s)

Gregory P. Fiumara, Wayne J. Salamon, Craig I. Watson

Abstract

With the proliferation of biometric-based identity management solutions, biometric algorithms need to be tested now more than ever. Independent biometric technology evaluations are needed to perform this testing, but are not trivial to run, as demonstrated by only a handful of organizations attempting to perform such a feat. Worse, many software development packages designed for running biometric technology evaluations available today shy away from techniques that enable automation, a concept that supports reproducible research. The evaluation software used for testing biometric recognition algorithms needs to efficiently scale as the sample datasets employed by researchers grow increasingly large. With better software, additional entities with their own biometric data collection repositories could easily administer a reproducible biometric technology evaluation. Existing evaluation software is available, but these packages do not always follow best practices and they are lacking several important features. This paper identifies the necessary requirements and ideal characteristics of a robust biometric evaluation toolkit and introduces our implementation thereof, which has been used in several large-scale biometric technology evaluations by multiple organizations.

Proceedings Title

The IEEE Seventh International Conference on Biometrics: Theory, Applications and Systems (BTAS 2015)

Conference Dates

September 8-11, 2015

Conference Location

Arlington, VA

Pub Type

Conferences

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Keywords

biometrics, performance evaluation, technology evaluation, software

Citation

Fiumara, G. , Salamon, W. and Watson, C. (2015), Towards Repeatable, Reproducible, and Efficient Biometric Technology Evaluations, The IEEE Seventh International Conference on Biometrics: Theory, Applications and Systems (BTAS 2015), Arlington, VA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=918459 (Accessed December 3, 2024)