Abstract
Accurate recognition of free text keystroke dynamics is challenging due to the unstructured and sparse nature of the data and its underlying variability. As a result, most of the approaches published in the literature on free text recognition, except for one recent one, have reported extremely high error rates. In this paper, we present a new approach for the free text analysis of keystrokes that combines monograph and digraph analysis, and uses a neural network to predict missing digraphs based on the relation between the monitored keystrokes. Our proposed approach achieves an accuracy level comparable to the best results obtained through related techniques in the literature, while achieving a far lower processing time. Experimental evaluation involving 53 users in a heterogeneous environment yields a false acceptance ratio (FAR) of 0.0152% and a false rejection ratio (FRR) of 4.82%, at an equal error rate (EER) of 2.46%. Our follow-up experiment, in a homogeneous environment with 17 users, yields FAR=0% and FRR=5.01%, at EER=2.13%.
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