Dynamic signature for a closed-set identification based on nonlinear analysis

Abstract

This paper presents a study of biometric identification using a methodology based on complexity measures. The identification system designed, implemented and evaluated uses nonlinear dynamic techniques such as Lempel-Ziv Complexity, the Largest Lyapunov Exponent, Hurst Exponent, Correlation Dimension, Shannon Entropy and Kolmogorov Entropy to characterize the process and capture the intrinsic dynamics of the user's signature. In the validation process 3 databases were used SVC, MCYT and our own (ITMMS-01) obtaining closed-set identification performances of 98.12%, 97.38% and 99.50% accordingly. Satisfactory results were achieved with a conventional linear classifier spending a minimum computational cost.