Selecting optimal personalized features for on-line signature verification using GA

For signature verification, there can be a large number of features available in a signature. However, not all these features are of use as some even could be unfavorable for verification of particular signatures. Finding an optimal personalized subset of all the possible features for a signer is crucial for signature verification systems, which are based on a parameter approach. This paper proposes a novel automated optimal personalized feature selection method based on genetic algorithms. Feature vectors for a specific signer are encoded into a population of genes or chromosomes. Through a process of genetic evolution with the application of specific genetic crossover and mutation operations an optimized personalized feature vector is obtained. An important characteristic of the method is that the number and type of features obtained for each signer through genetic evolution is not fixed and predetermined. Experimental results are presented to demonstrate the effectiveness of this novel approach to automated signature verification.