June 20, 2012 -
Dynamic signature is a biometric modality that uses, for recognition purposes, the anatomic and behavioral characteristics that an individual exhibits when signing his or her name (or other phrase).
Dynamic signature devices should not be confused with electronic signature capture systems that are used to capture a graphic image of the signature and are common in locations where merchants are capturing signatures for transaction authorizations.
Data such as the dynamically captured direction, stroke, pressure, and shape of an individual’s signature can enable handwriting to be a reliable indicator of an individual’s identity (i.e., measurements of the captured data, when compared to those of matching samples, are a reliable biometric for writer identification.)
The first signature recognition system was developed in 1965. Dynamic signature recognition research continued in the 1970s focusing on the use of static or geometric characteristics (what the signature looks like) rather than dynamic characteristics (how the signature was made). Interest in dynamic characteristics surged with the availability of better acquisition systems accomplished through the use of touch sensitive technologies.
In 1977, a patent was awarded for a “personal identification apparatus” that was able to acquire dynamic pressure information.
Dynamic signature recognition uses multiple characteristics in the analysis of an individual’s handwriting. These characteristics vary in use and importance from vendor to vendor and are collected using contact sensitive technologies, such as PDAs or digitizing tablets.
Most of the features used are dynamic characteristics rather than static and geometric characteristics, although some vendors also include these characteristics in their analyses. Common dynamic characteristics include the velocity, acceleration, timing, pressure, and direction of the signature strokes, all analyzed in the X, Y, and Z directions.
The X and Y position are used to show the changes in velocity in the respective directions while the Z direction is used to indicate changes in pressure with respect to
Some dynamic signature recognition algorithms incorporate a learning function to account for the natural changes or drifts that occur in an individual’s signature over time.
The characteristics used for dynamic signature recognition are almost impossible to replicate. Unlike a graphical image of the signature, which can be replicated by a trained human forger, a computer manipulation, or a photocopy, dynamic characteristics are complex and unique to the handwriting style of the individual. Despite this major strength of dynamic signature recognition, the characteristics historically have a large intra-class variability (meaning that an individual’s own signature may vary from collection to collection), often making dynamic signature recognition difficult. Recent research has reported that static writing samples can be successfully analyzed to overcome this issue.