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 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.