Abstract
With the development of pen-based mobile device, on-line signature verification is gradually becoming a kind of important biometrics verification. This thesis proposes a method of verification of on-line handwritten signatures using both Support Vector Data Description (SVM) and Genetic Algorithm (GA). A 27-parameter feature set including shape and dynamic features is extracted from the on-line signatures data. The genuine signatures of each subject are treated as target data to train the SVM classifier. As a kernel based one-class classifier, SVM can accurately describe the feature distribution of the genuine signatures and detect the forgeries. To improving the performance of the authentication method, genetic algorithm (GA) is used to optimise classifier parameters and feature subset selection. Signature data form the SVC2013 database is used to carry out verification experiments. The proposed method can achieve an average Equal Error Rate (EER) of 4.93% of the skill forgery database.
Highlights
With the rapid development of e-commerce, personal communication, computer, Internet, mobile devices with a handwriting input function more popularization, provide favorable conditions for the development of the use of online handwritten signature application [1]
This paper introduces a description for the online signature verification method based on support vector machine data classifier
Classification of data description algorithm is a class of typical single value, support vector machine data presented by Tax and Duin description of theory development is already quite mature [11]
Summary
With the rapid development of e-commerce, personal communication, computer, Internet, mobile devices with a handwriting input function more popularization, provide favorable conditions for the development of the use of online handwritten signature application [1]. Online authentication method can accurately obtain relevant information according to the actual characteristics of the signature based on the construction of appropriate statistical models or signature template. There are often many feature extraction in a system with redundant information, and even the interference information data, if they are flowing into the classifier, which will seriously affect the treatment effect of classifier, and can enhance the complexity of the system to a great extent In this case, we must find the ideal feature subset has high classification performance, while minimizing the subset should be able to realize the characteristic dimension. In the matching of signature verification, dynamic time warping (DTW) and hidden Markov model (HMM) and artificial neural network classifier and the Gauss is a commonly used method These signature authentication methods have achieved good results in the experiment, but still facing how to adapt to the change of signature handwriting process or need more training samples problem. Signature verification experiments can be done in a public signature database
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