In this paper a new approach will be introduced to identify pen-based digitizer devices based on handwritten samples
used for biometric user authentication. This new method of digitizer identification based on their signal properties can
also be seen as an influencing part in the new research area of so-called sensometrics. The goal of the work presented in
this paper is to identify statistical features, derived from signals provided by pen-based digitizer tablets during the
writing process, which allow identification, or at least group discrimination of different device types. Based on a
database of a total of approximately 40,000 writing samples taken on 23 different pen digitizers, specific features for
class discrimination will be chosen and a novel feature vector based classification system will be implemented and
experimentally validated. The goal of our experimental validation is to study the class space that can be obtained, given
a specific feature set, i.e. to which degree single tablets and/or groups of pen digitizers can be identified using our
developed classification by a decision tree model. The results confirm that a group discrimination of devices can be
achieved. By applying this new approach, the 23 different tablets from our database can be discriminated in 19 output
groups.
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