Identification of Imitative Signature Writers Based on Fine-grained Dynamic Features
Electronic signature gradually replaces traditional handwriting,and the authentication of electronic sig-nature has become a difficult problem in the field of public security and judicial authentication.In this paper,a fine-grained electronic signature handwriting dynamic feature extraction method was proposed,and supervised learning methods including K-nearest neighbor,decision tree,random forest and support vector machine were used to compre-hensively analyze the dynamic features and static features of the imitation electronic signature.A classification model of the imitation electronic signature was established.The experiment results demonstrate that the writer recognition model based on K-nearest neighbor algorithm has the best performance,whose accuracy rate,precision rate,recall rate and the AUCis 0.917,0.906,0.871,and0.965,respectively.The experiment shows that the dynamic features of electronic handwriting can significantly improve the performance of writer classification model,the model's recognition ability declines when the categories of training sample or the number of training set decreasing.
Electronic signatureDynamic feature of handwritingMachine learningImitating signature
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