Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

An Introduction to Biometric-completeness: The Equivalence of Matching and Quality

Published

Author(s)

P J. Phillips, J. R. Beveridge

Abstract

This paper introduces the concept of biometric-completeness. A problem is biometric-complete if solving the problem is equivalent to solving a biometric recognition problem. The concept of biometric-completeness is modeled on the informal concept of artificial intelligence (AI) completeness. The concept of biometric-completeness is illustrated by showing a formal equivalence between biometric recognition and quality assessment of biometric samples. The model allows for the inclusion of quality of biometric samples in verification decisions. The model includes most methods for incorporating quality into biometric systems. The key result in this paper shows that finding the perfect quality measure for any algorithm is equivalent to finding the perfect verification algorithm. Two results that follow from the main result are: finding the perfect quality measure is equivalent to solving the open-set and closed-set identification problems; and that a universal perfect quality measure cannot exist.
Proceedings Title
IEEE Third International Conference on Biometrics: Theory, Applications and Systems
Conference Dates
September 28-30, 2009
Conference Location
Crystal City, VA

Citation

Phillips, P. and Beveridge, J. (2009), An Introduction to Biometric-completeness: The Equivalence of Matching and Quality, IEEE Third International Conference on Biometrics: Theory, Applications and Systems , Crystal City, VA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=903122 (Accessed April 25, 2024)
Created June 16, 2009, Updated February 19, 2017