Window Functions in Rhythm Based Biometric Authentication

Emerging new technologies and new threads entail additional security; therefore, biometric authentication is the key to protecting the passwords by classifying unique biometric features. One of the protocols recently proposed is the rhythm-based authentication dealing with the dominant frequency components of the keystroke signals. However, frequency component itself is not enough to understand the whole keystroke sequence; therefore, the characteristic of a password could only be analyzed by transformations providing the information of when which dominant frequency arises. In this paper, the biometric signal generated from keystroke data is divided into windows by various window functions and sizes for frequency and time localization. As a guide for signal processing in biometrics and biomedicine, we compared Hamming and Blackman widow functions with various sizes in short time Fourier transformations of the signals and found out that Blackman is more appropriate for biometric signal processing.