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Transformation, Ranking, and Clustering for Face Recognition Algorithm Comparison

Published

Author(s)

Stefan D. Leigh, Nathanael A. Heckert, Andrew L. Rukhin, J G. Phillips, Elaine M. Newton, M Moody, K Kniskern, S Heath

Abstract

The performance of face recognition algorithms is recently of increased interest, although to date empirical analyses of algorithms have been limited to rank-based scores such a cumulative match score and receiver operating characteristic. This paper demonstrates that algorithms that report ratio scale similarities between unknown and gallery images can be normalized so that a large body of classical statistical methods can be applied to measure recognition performance.
Proceedings Title
Third Workshop on Automatic Identification Advanced Technologies
Conference Dates
March 14-15, 2002
Conference Location
Tarrytown, NY

Keywords

face, face recognition, algorithms, biometrics

Citation

Leigh, S. , Heckert, N. , Rukhin, A. , Phillips, J. , Newton, E. , Moody, M. , Kniskern, K. and Heath, S. (2002), Transformation, Ranking, and Clustering for Face Recognition Algorithm Comparison, Third Workshop on Automatic Identification Advanced Technologies, Tarrytown, NY, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=51043 (Accessed March 5, 2024)
Created March 1, 2002, Updated February 17, 2017