The collection of human biometric test data for system development and evaluation within any chosen modality generally requires significant time and effort if data are to be obtained in workable quantities. To overcome this problem, techniques to generate synthetic data have been developed. This paper describes a novel technique for the automatic synthesis of human handwritten-signature images, which introduces modeled variability within the generated output based on positional variation that is naturally found within genuine source data. The synthesized data were found to generate similar verification rates to those obtained using genuine data with the use of a commercial verification engine, thereby indicating the suitability of the data synthesized by using this method for a wide range of application scenarios.
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