A complete statistical model of a handwritten signature as an object of biometric identification

Fuzzy extractors and neural networks used for identification by handwriting dynamics should be used in tandem with another methods. Bayesian statistical inference networks or other networks able to consider a complete matrix of correlation coefficients may become a method that complete them. The paper proposes a simple correlation metric that takes into account correlation coefficients of biometric data in an explicit form. A mean vector, a standard deviation vector and a correlation matrix of biometric parameters should be considered as a complete statistical model of a handwritten signature. These data are enough for tuning a variety of variants of statistical inference networks using Bayes rules.