A complete statistical model of a handwritten signature as an object of biometric identification
Alexander Ivanov, E. I. Kachajkin, П. С. Ложников · 2016
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.