Biometrics Authentication of Aerial Handwritten Signature Using a Convolutional Neural Network

Abstract

Recent years, biometrics authentication is receiving attention by development of information society. In this paper, we propose a personal authentication system, which uses behavior characteristics among biometrics. We focus on aerial handwritten signature, because it is difficult to forge it, and there is no risk of loss. In this paper, we measure signatures using Leap Motion Controller. It can measure three dimensional space coordinates with high accuracy. We divide signature data into three axial directions of coordinates XYZ in order to use them as one-dimensional data. We carry out preprocessing to signature data and normalize them. Next, we use deep learning based on a convolutional neural network for feature extraction and identification. In this experiment, we prepare data obtained from six subjects. We obtain genuine data of one subject. The remaining five subjects are used to create forgery data. We classify signature data into two classes. We conduct deep learning in which CNN carries out 10,000 cycles learning in one trial. We carry out this trial 5 times and evaluate mean accuracy by cross validation for two types of genuine data. The average discrimination accuracy of this experiment are 97.0 % and 95.9%. In addition, the false rejection rates are 9.6% and 19.2%. The false acceptance rate are 0.8% and 0.1%. Recent years, biometrics authentication is receiving attention by development of information society. In this paper, we propose a personal authentication system, which uses behavior characteristics among biometrics. We focus on aerial handwritten signature, because it is difficult to forge it, and there is no risk of loss. In this paper, we measure signatures using Leap Motion Controller. It can measure three dimensional space coordinates with high accuracy. We divide signature data into three axial directions of coordinates XYZ in order to use them as one-dimensional data. We carry out preprocessing to signature data and normalize them. Next, we use deep learning based on a convolutional neural network for feature extraction and identification. In this experiment, we prepare data obtained from six subjects. We obtain genuine data of one subject. The remaining five subjects are used to create forgery data. We classify signature data into two classes. We conduct deep learning in which CNN carries out 10,000 cycles learning in one trial. We carry out this trial 5 times and evaluate mean accuracy by cross validation for two types of genuine data. The average discrimination accuracy of this experiment are 97.0 % and 95.9%. In addition, the false rejection rates are 9.6% and 19.2%. The false acceptance rate are 0.8% and 0.1%.