Authentication of Aerial Input Numerals by Leap Motion and CNN

As information technology has advanced in recent years, services which include personal authentication systems such as ATM are increasing. Current main personal authentication systems include IC cards, passwords, and biometrics authentication such as fingerprint authentication. However, there are several problems in these systems. Therefore, better systems are needed. As such systems, we propose a method to write numerals in the air using the Leap motion and to carry out personal authentication from such aerial handwriting data. We try to authenticate numerals 0 to 9 which are written by three subjects. After applying some pre-processing to inputs, learning and identification are carried out using CNN which is a method of machine learning. As a result, average identification accuracy was 90.3%. From this result, it is suggested that input numerals in the air can be authenticated and there is a possibility to construct a new personal authentication system.