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Personal Identification and Authentication Based on Keystroke Dynamics in Japanese Long-Text Input
Abstract
We have investigated several characteristics of keystroke dynamics in Japanese long-text input. We performed experiments with 189 participants, classified into three groups according to the number of letters they could type in five minutes. In this experimental study, we extracted feature indices from the keystroke timing for each alphabet letter and for each two-letter combination composed of a consonant and vowel in Japanese text. Taking into account two identification methods using Weighted Euclidean Distance (WED) and Array Disorder (AD), we proposed a hybrid model for identifying individuals on the basis of keystroke data in Japanese long-text input. By evaluating the identification performance of individuals in the three groups, the effectiveness of the method was found to correspond to the typing skill level of the group.
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