Handwriting
Handwriting or “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).
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.)
Automated handwriting recognition tools have been proven, with some of the superior technologies, to produce results that are more reliable than the best Forensic Handwriting Experts (FHE). In the 2011 Signature Competition the tested technologies varied considerably with the best producing FRR of 2.47% and FAR of 2.19%, while the worst produced FRR of 50% and FAR of 47.41%.
These result establish that like facial recognition, handwriting technologies are not reliable enough to identify people with the reliability of DNA, fingerprint, retina, vascular and iris.
More at:
Dynamic Signature
First International Workshop on Automated Forensic Handwriting Analysis
SigComp 2011
The Effects of Constraint on a Signature’s Static and Dynamic Features
Handwriting or “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).
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.)
Automated handwriting recognition tools have been proven, with some of the superior technologies, to produce results that are more reliable than the best Forensic Handwriting Experts (FHE). In the 2011 Signature Competition the tested technologies varied considerably with the best producing FRR of 2.47% and FAR of 2.19%, while the worst produced FRR of 50% and FAR of 47.41%.
These result establish that like facial recognition, handwriting technologies are not reliable enough to identify people with the reliability of DNA, fingerprint, retina, vascular and iris.
More at:
Dynamic Signature
First International Workshop on Automated Forensic Handwriting Analysis
SigComp 2011
The Effects of Constraint on a Signature’s Static and Dynamic Features