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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