An Investigation of Predictive Profiling from Handwritten Signature Data

Although it has long been recognized that non-biometric factors (for example, general demographic characteristics) can have an impact on the performance of automated person identification systems, such information is not routinely adopted in most practical biometric processing. In forensic applications, however, such additional information may be exploited most productively, since typical scenarios require the prediction of a wide range of individual characteristics from a range of available samples, biometric and non-biometric. In many such situations, it may be very useful to predict characteristics short of actual identity, since this type of prediction can significantly increase the amount of evidence available. In this paper we provide some benchmarking data to demonstrate the extent to which typical non-biometric information might be effective in practice in this context, and illustrate the differential effects depending on the classifier adopted. In particular, however, we investigate experimentally how traditionally measured handwritten signature data might be used to predict non-biometric categories in a way which can be exploited a variety of practical application scenarios.