Verification of the Usefulness of Personal Authentication with Aerial Input Numerals Using Leap Motion

With the progress of IoT, everything is going to be connected to the network. It will bring us a lot of benefits however some security risks will be occurred by connecting network. To avoid such problems, it is indispensable to strengthen security more than now. We focus on personal authentication as one of the security. As a security enhancement method, we proposed a method to carry out numeral identification and personal authentication using numerals written in the air with Leap motion sensor. In this paper, we also focus on proper handling of aerial input numerals to verify whether the numerals written in the air are helpful for authentication. We collect numerals 0 to 9 from five subjects, then apply three pre-processing to these data, learn and authenticate them by CNN (convolutional neural network) which is a method of machine learning. As a result of learning, an average authentication accuracy was 92.4%. This result suggests that numerals written in the air are possible to carry out personal authentication and it will be able to construct a better authentication system

Online signature verification techniques

Signature is a behavioral biometric: it is not based on the physical properties, such as fingerprint or face, of the individual, but behavioral ones. Signature verification is split into two according to the available data in the input. Offline (static) signature verification takes as input the image of a signature and is useful in automatic verification of signatures found on bank checks and documents. Online (dynamic) signature verification uses signatures that are captured by pressure-sensitive tablets that extract dynamic properties of a signature in addition to its shape. The purpose of project is to develop an authentication system based on personal signatures. Signature verification is an important research topic in the area of biometric authentication. In this project the work is done in such a way that the signatures are captured using WEBCAM. A visual-based online signature verification system in which the signer’s pen tip is tracked. The data acquisition of the system consists of only low-cost cameras (webcams) and does not need special equipment such as an electronic tablet. Online signature data is obtained from the images captured by the webcams by tracking the pen tip. The pen tip tracking is implemented by the Sequential Monte Carlo method in real time. Then, the distance between the input signature data and reference signature data enrolled in advance is computed using Dynamic Time Warping (DTW). Finally, the input signature is classified as genuine or a forgery by comparing the distance with a threshold