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Transformation, Ranking, and Clustering for Face Recognition Algorithm Performance

Published

Author(s)

Stefan D. Leigh, Nathanael A. Heckert, Andrew L. Rukhin, P J. Phillips, Patrick J. Grother, E 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 enormalized so that a large body of classical statistical methods can be applied to measure recognition performance.
Citation
Automatic Identification Advanced Technologies Workshop

Keywords

ANOVA, biometric evaluation, face recognition

Citation

Leigh, S. , Heckert, N. , Rukhin, A. , Phillips, P. , Grother, P. , Newton, E. , Moody, M. , Kniskern, K. and Heath, S. (2002), Transformation, Ranking, and Clustering for Face Recognition Algorithm Performance, Automatic Identification Advanced Technologies Workshop (Accessed April 18, 2024)
Created January 1, 2002, Updated February 17, 2017