Personal Identification by Flick Input Using Self-Organizing Maps with Acceleration Sensor and Gyroscope

Screen lock is vulnerable against shoulder surfing since password, personal identification numbers (PIN) and pattern can be seen when smart phones are used in public space although important information is stored in them and they are often used in public space. In this paper, we propose a new method in which passwords are combined with biometrics authentication which cannot be seen by shoulder surfing and difficult to be guessed by brute-force attacks. In our method, the motion of a finger is measured by sensors when a user controls a mobile terminal, and the motion which includes characteristics of the user is registered. In our method, registered characteristics are classified by learning with self-organizing maps. Users are identified by referring the self-organizing maps when they input passwords on mobile terminals.