Document Typist Identification by Classification Metrics Applying Keystroke Dynamics Under Unidealised Conditions

Keystroke Dynamics is the study of typing patterns and rhythm for personal identification and traits. Keystrokes may be analysed as fixed text such as passwords or as continuous typed text such as documents. This paper reviews different classification metrics for continuous text, such as the A and R metrics, Canberra, Manhattan and Euclidean and introduces a variant of the Minkowski distance. To test the metrics, we adopted a substantial dataset containing 239 thousand records acquired under real, harsh, and unidealised conditions. We propose a new parameter for the Minkowski metric, and we reinforce another for the A metric, as initially stated by its authors.