In this paper, we present an empirical evaluation of 30 features used in touch-based continuous authentication. It is essential to identify the most significant features for each user, as behaviour is different amongst humans. Thus, a fixed feature set cannot be applied to all models. We highlight this importance by selecting features accordingly using our approach, seeking to individually select and empirically test the discriminative power of a range of features as well as feature interaction in the context of individual users. We test five different feature selection techniques: Mutual Information, Sequential Forward Selection, Sequential Floating Forward Selection, Sequential Backwards Selection, and Sequential Floating Backwards Selection. Our results show that a unique set of features can be selected for each user, while increasing or maintaining performance, i.e. up to 27 out of 30 features were removed for one user without affecting performance. We also show that distinctive features should be evaluated on a user basis, as particular features may be significant for some, while redundant for others. Moreover, for each user, the same features are selected for horizontal and vertical strokes while performance persists when using a horizontal model to predict vertical behaviour and vice versa.