On-Line Recognition of Handwritten Symbols
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Automatic On-line Signature Verification
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Applying personalized weights to a feature set for on-line signature verification
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Online Handwritten Signature Verification for Electronic Commerce over the Internet
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
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For signature verification, there can be a large number of features available in a signature and not all these features are of use as some even could be unfavorable for verification of particular signatures. Also, the large number of features imposes difficulty in comparing instances. This may be worsened if observed peculiarities between features could not be drawn to benefit the comparison process. Hence, to determine a personalized subset of features and allowing for the inclusion of heuristics in verification are prime factors to consider in the design of the verification model. In this novel approach, genetic algorithm determines the optimal personalized features for each subject and fuzzy logic transforms the heuristics conceived by a human verifier to rules that govern the behavior of the automated verification system. Experimental results are presented to demonstrate the effectiveness of this scheme.