In this paper we propose handwritten signature classification using a supervised chromosome clustering technique. Due to the time variant nature of handwriting of human being, a set of hundred sample handwritten signatures were first collected from the user or individual in form of the same sized grayscale images. These grayscale handwritten signature images will be used as the training set in our classification algorithm. Our proposed algorithm will then decide whether the future incoming handwritten signature of an individual can be a member of the training set or not. In this paper, the distance and similarities play an important role, where the greater the dissimilarity measure or distance of genes, the more dissimilar are the two chromosomes.
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