Centre for Pattern Recognition and Machine Intelligence (CENPARMI)
Abstract
Proceedings of the International Conference on Frontiers in Hadwriting Recognition (ICFHR 2008)A dynamic signature verification system based on Hidden
Markov Models is presented. For each user model,
the number of states and Gaussian mixtures of the Hidden
Markov Model is automatically set in order to optimize
the verification performance. By introducing this userdependent
structure in the statistical modeling of signatures,
the system error rate is significantly decreased in
the challenging scenario of dynamic signature verification
on handheld devices. Experimental results are given on a
subset of the recently acquired BIOSECURE multimodal
database, using signatures captured with a PDAThis work has been supported by the Spanish Ministry of Education under project TEC2006-13141-C03-03