Handwrite signature characterization and verification using time causal Information Theory quantifiers

A signature is a handwritten depiction of someone's name or some other mark of identification that person writes on documents or a device as proof of identification. The formation of signature varies from person to person or even from the same person due to the psychophysical state of the signer and the conditions under which the signature apposition process occurs.
Handwritten signature characterization and verification is a behavioral biometric modality
that relies on a rapid personal gesture.
Each hand-drawn signature has a level of complexity which depends on the author.
Among all the biometric traits that can be categorized as pure
behavioral, the signature is the one that has the widest social
acceptance for identity authentication.
Online signature verification
allows the introduction of the signature's dynamic information, not
just the outcome of the signing process.
Such dynamical information is captured by a digitizer, and generates
``online" signatures, namely a sequence of sampled points during the
signing process: $\big(x,y\big)(t)$, the coordinate $x$ and $y$ at time $t$.
We compute Information-Theoretic measures (Shannon Entropy,
Generalized Statistical Complexity and Fisher information measure), on
the Bandt-Pompe nonparametric descriptor, which take into account the time causal
of the corresponding time series.
These measures are used as the input features of a signature characterization and
verification system, whose performance is assessed over the well
known MCTY 100 signature data base.
Our results are competitive in terms of acceptance and rejection
errors, and is shown very attractive in terms of computational
requirements.