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Biometric measurements are now often routinely adopted as a robust means of determining individual identity. Such an approach is clearly beneficial in a variety of scenarios, including those relating to medical environments. In the medical context, however, the use of biometric data can potentially offer other valuable opportunities for harnessing the power of biometrics which have a more direct bearing on healthcare monitoring and treatment delivery. In this paper we focus on the prediction of "soft" biometric data and, in particular, we describe an approach which aims to predict "higher level" characteristics about an individual, such as those which may broadly be described as emotional or mental state. We show how such a capability can be utilised in healthcare scenarios, and specifically, by presenting some initial analysis of results from newly acquired data in a keystroke-based data collection task, we identify the most crucial issues which must be addressed if our basic predictive technique is to be developed for practical viability.

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This paper proposes and investigates experimentally an approach to age prediction from iris images by using a combination of a small number of very simple geometric features, and a more versatile and intelligent classifier structure which can achieve accuracies to 75%. To our knowledge, this is the first experimental study of three class age prediction from iris images.