This paper uses the keystroke dynamics in user authentication. The relationship between the distance metrics and the data template, for the first time, was analyzed and new distance based algorithm for keystroke dynamics classification was proposed. The results of the experiments on the CMU keystroke dynamics benchmark dataset1 were evaluated with an equal error rate of 0.0614. The classifiers using the proposed distance metric outperform existing top performing keystroke dynamics classifiers which use traditional distance metrics.
Ngoc Tran Nguyen,
"Distance-based classification of keystroke dynamics", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111E (11 July 2016); doi: 10.1117/12.2242022; https://doi.org/10.1117/12.2242022