Estimation of User Specific Parameters in One-class Problems

In this paper, we propose a method to find and use user-dependant parameters to increase the performance of a keystroke dynamic system. These parameters include the security threshold and fusion weights of different classifiers. We have determined a set of global parameters which increase the performance of some keystroke dynamics methods. Our experiments show that parameter personalization greatly increases the performance. The main problem is how to estimate the parameters from only a user training set containing ten login sequences. This problem is a promising way to increase performance in biometric but it is still an open path