The biometric menagerie, or biometric zoo, is a classification system used to label the matching tendencies of a given subject's biometric signature. These tendencies may include matching their own signatures poorly or matching other subjects' signatures better than their own. Several experiments show the biometric menagerie to be an unstable classification system where subjects frequently change class labels. In an attempt to improve the stability of the biometric menagerie, existing score normalization techniques are expanded to create Covariate F-Normalization (CovF-Norm). When the normalization methods are applied to the biometric menagerie, the classification system remains unstable and unreliable for practical use with subject-specific thresholding. The new normalization method, CovF-Norm, is also shown to be algo- rithm independent and data set independent unlike the biometric menagerie which is dependent on both the algorithm and data set. CovF-Norm is shown to significantly improve performance when compared to the standard F-Normalization technique's equal error rate.