In [31], the authors studied which aspects have the major influence on keystroke dynamics performance.
In view of the fact that performance in keystroke dynamics is highly dependent on the dataset, the identification of benchmarking datasets turns out to be fundamental.
The Disklavier can be played in the traditional way, but it can also record the player's keystrokes and repeat the performance on its own, much as an early 20th-century player piano reproduced music cut into a piano roll.
The following subsections are organized in such a way to answer each of the research sub-questions: advantages and disadvantages of keystroke dynamics, feature extraction, classification algorithms, performance evaluation and benchmarking datasets.
Another fundamental issue in performance evaluation is regarding the way keystroke data is collected.
The present study empirically tests the keystroke-level model (KLM) for predicting the time performance of basic interaction controls on the touch-sensitive smartphone interface (i.e., tapping, pointing, dragging, and flicking).
Several aspects may affect the performance of a biometric system based on keystroke dynamics.
