We overview biometric authentication and present a system
for on-line signature verification, approaching the problem as a two-class
pattern recognition problem. During enrollment, reference signatures are
collected from each registered user and cross aligned to extract statistics
about that user’s signature. A test signature’s authenticity is established
by first aligning it with each reference signature for the claimed user.
The signature is then classified as genuine or forgery, according to the
alignment scores which are normalized by reference statistics, using standard
pattern classification techniques. We experimented with the Bayes
classifier on the original data, as well as a linear classifier used in conjunction
with Principal Component Analysis (PCA). The classifier using
PCA resulted in a 1.4% error rate for a data set of 94 people and 495
signatures (genuine signatures and skilled forgeries).