Hybrid solution for the feature selection in personal identification problems through keystroke dynamics

Techniques based on biometrics have been successfully applied to personal identification systems. One rather promising technique uses the keystroke dynamics of each user in order to recognize him/her. In this work, we present the development of a hybrid system based on support vector machines and stochastic optimization techniques. The main objective is the analysis of these optimization algorithms for feature selection. We evaluate two optimization techniques for this task: genetic algorithms (GA) and particle swarm optimization (PSO). In the present study, PSO outperformed GA with regard to classification error and processing time, but was inferior regarding the feature reduction rate.