  Building upon findings in computational model of handwriting learning and
execution, we introduce the concept of stability to explain the difference
between the actual movements performed during multiple execution of the
subject's signature, and conjecture that the most stable parts of the signature
should play a paramount role in evaluating the similarity between a questioned
signature and the reference ones during signature verification. We then
introduce the Stability Modulated Dynamic Time Warping algorithm for
incorporating the stability regions, i.e. the most similar parts between two
signatures, into the distance measure between a pair of signatures computed by
the Dynamic Time Warping for signature verification. Experiments were conducted
on two datasets largely adopted for performance evaluation. Experimental
results show that the proposed algorithm improves the performance of the
baseline system and compares favourably with other top performing signature
verification systems.
