On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Signature Verification Using a Computational Intelligence Approach
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Class-dependent feature weights evaluation
CompSysTech '07 Proceedings of the 2007 international conference on Computer systems and technologies
Development of a Sigma-Lognormal representation for on-line signatures
Pattern Recognition
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For signature verification, there can be a large number of useful features including pen-speed, pen-pressure, etc. However, all these features may not be useful or even be harmful for a given person when not enough number of training samples are allowed. Namely, for an optimal verification, each person requires a different subset of all the possible features. Finding an optimal subset for an individual needs too much combinatorial efforts. Instead, we propose an approach using all the same features for every person by assigning a different weight to each feature according to the nature of the person's signature.