MPhil Thesis Defence


"Online Handwritten Signature Verification with Hidden Markov Models and
Deformable Models"

By

Mr. Ka-Wing Cho


Abstract

Offline handwritten signature verification has been used for user identity
authentication for a long time.  Nowadays, the reliability of this method
is being challenged, as skillful forgers can produce forged signatures
that cannot be distinguished from genuine ones easily.  However, signature
verification becomes much more reliable if the online approach is taken by
incorporating dynamic features, such as signing pressure, speed, pen-tilt,
stroke order and air movement, as these features are "hidden" in the sense
that they cannot be revealed by simply observing the shape of the
signature.

Two different models are commonly used for pattern matching applications.
The first one is based on hidden Markov models (HMM) for time-varying
patterns and the second one is based on deformable models (DM) for
shape-varying patterns.  Although HMMs have been demonstrated to be very
effective for handwritten character recognition and speech recognition,
the statistical nature of HMMs requires that sufficient data be available
for model training, which is not a reasonable assumption to take for
biometric applications such as signature verification.  This calls for the
DM approach which does not require many training patterns for model
construction.

In this thesis, we have performed a number of experiments for the problem
of online handwritten signature verification using both the HMM and DM
approaches.  Experimental results show that the HMM approach is faster and
more accurate than the DM approach if the same number of signatures are
used for model construction.  However, the non-parametric DM approach is
more flexible because a reference model can be built from one single
signature only, although multiple reference models can be used if more
signatures are available for model construction.


Date:			Wednesday, 7 November 2001

Time:			10:00a.m.-12:00noon

Venue:			Room 4334
			Lift 3

Committee Members:	Dr. Dit-Yan Yeung (Supervisor)
			Dr. James Kwok (Chairman)
			Dr. Brian Mak


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