Personal authentication method based on unconscious actions in swiping motion

In the present paper, we investigate a novel personal authentication method using unconscious personal features of a swipe motion in the pattern lock authentication on Android devices. As unconscious personal features, we employ a velocity variation of the finger movement and the contact area of the finger on the screen during the swiping motion. In order to show the latent individual characteristics in these features, we have conducted t-tests and the result shows that about 98 % of the test points have significant differences. This indicates that the extracted features are unconscious features latent in the swiping operation. In our personal authentication method based on unconscious features in swipe motion, we have applied standardized Euclidean distance. In authentication experiments, we determine appropriate threshold values for the distance from training and validation data, we evaluate the false rejection rate (FRR) and the false acceptance rate (FAR). The best cases of FRR and FAR are 0 % and 0 %, and the worst cases are 4 % and 0 %, respectively. In smartphone personal authentication, it is crucial to minimize the FAR as much as possible, making our authentication method practical for personal authentication on smartphones.