ABSTRACT
This chapter investigates the integration of the soft biometric features, gender and age group, with the existing keystroke dynamics user authentication systems. It also investigates the probability of predicting gender and age group based on typing pattern. The main objective of this chapter is to develop a model that can identify the gender and age group of users through the way of typing on a computer keyboard and touching a computer screen for a predefined text, and it increases the accuracy by recognizing this soft biometric information as additional features in keystroke dynamics user authentication systems. The chapter further fuses the two soft biometric scores with the timing features to enhance the performance of keystroke dynamics user authentication systems. It is also observed that gender and age group information as extra features increase the user authentication performance instead of using only gender information.
