Keystroke dynamics allows to authenticate individuals through their way of typing on a computer keyboard. In this study, we are interested in static shared secret keystroke dynamics (all the users type the same password). We present new soft biometrics information which can be extracted from keystroke typing patterns: the gender of the user. This is the first study, to our knowledge, experimenting such kind of information in the field of keystroke dynamics. We present a method for gender recognition through keystroke dynamics with more than 91% of accuracy, on the tested dataset, and we show the improvement on keystroke dynamics authentication method using such kind of information through pattern and score fusion. We obtain a gain of 20% when using gender information against a classical keystroke dynamics method.
Giot and Rosenberger's REF research introduces a new soft biometric for keystroke dynamics based on gender recognition.
A new soft biometric approach for keystroke dynamics based on gender recognition
IJITM
IJITM
Computer Science