Biometric Authentication of Age and Gender prediction using GREYC Keystroke Dynamics Dataset
Keywords:
Biometrics, keystroke dynamics, Soft biometrics, authentication, Back Propagation Neural Network (BPNN), Discrete Wavelet Transforms (DWT).Abstract
Keystroke dynamics allows to authenticate individuals through their way of typing on a computer keyboard. In this study, this paper interested in static shared secret keystroke dynamics (all the users type the same password). It can be combined with passphrases authentication resulting in a more secure verification system. This paper presents a new soft biometrics information which can be extracted from keystroke dynamics patterns: The Age and gender of the user when he/she types a given password or passphrase on a keyboard. This experiments were conducted on a web based keystroke dynamics database of 118 users and our experiments on keystroke authentication it exploits the features from 2D Discrete wavelet transformation (DWT) to characterize the keystroke dynamics, and provides results from classification algorithms. BPNN classifier it obtained best results achieved were 86.2% accuracy respectively.
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