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Dependence Characteristics of Face Recognition Algorithms

Published

Author(s)

Andrew L. Rukhin, Patrick J. Grother, P J. Phillips, Stefan D. Leigh, E M. Newton, Nathanael A. Heckert

Abstract

Nonparametric statistics for quantifying dependence between the output rankings of face recognition algorithms are described. Analysis of the archived results of a large face recognition study shows that even the better algorithms exhibit significantly different behaviors. It is found that there is significant dependence in the rankings given by two algorithms to similar {\em and} dissimilar faces but that other samples are ranked independently. A class of functions known as copulas is used; it is shown that the correlations arise from a mixture of two copulas.
Proceedings Title
International Conference on Pattern Recognition (ICPR), 2002
Volume
2
Conference Dates
August 12-15, 2002
Conference Location
quebec

Keywords

algorithm comparison, empirical evaluation, face recognition, rank correlation

Citation

Rukhin, A. , Grother, P. , Phillips, P. , Leigh, S. , Newton, E. and Heckert, N. (2002), Dependence Characteristics of Face Recognition Algorithms, International Conference on Pattern Recognition (ICPR), 2002, quebec, -1 (Accessed July 25, 2024)

Issues

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Created January 1, 2002, Updated February 19, 2017