Abstract
In this work, variable length login-id and password belonging to the user were analyzed for bringing forth a more secure verification system. Soft biometrics such as age group and gender are estimated from keystroke dynamics patterns when he/she types a given password or login id on a keyboard. Experiments were carried on GREYC a web-based keystroke dataset by exploiting the features from DWT of keystroke dynamics and provides classification results using PSO optimized neural network. Experiments done using PSO-NN resulted in 94% accuracy which clearly out performs the BPNN and GA-NN classifiers.
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