Time-frequency analysis of keystroke dynamics for user authentication

Abstract With the increasing need for information security, one of the key options ahead is to provide security based on biometrics. Authentication based on keystroke dynamics is a low cost and convenient biometric authentication technique. In this paper, a method for user authentication based on keystroke dynamics with a novel similarity measure is introduced. Using time–frequency analysis, a similarity measure between an input sample and user reference samples is directly obtained. The input sample is initially converted to a keystroke dynamics signal. Dynamic time warping method is applied to equalize the length of signals. Then, using Wigner distribution, the time–frequency representation of the samples is obtained. Finally, exploiting the correlation coefficient, the similarity between two signals in the time–frequency domain is measured. We also added an update procedure to the proposed method to enhance its performance. The performance of the proposed method is investigated and compared with the state-of-the-art methods. Experimental results show the superiority of the proposed method.

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