文章基本信息

标题：Convolutional Neural Network for Offline Signature Verification via Multiple Classifiers
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作者：Fadi Mohammad Alsuhimat ; Fatma Susilawati Mohamad
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2021
卷号：11
期号：20
语种：English
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The signature process is one of the most significant processes used by organizations to preserve the security of information and protect it from unwanted penetration or access. As organizations and individuals move into the digital environment, there is an essential need for a computerized system able to distinguish between genuine and forged signatures in order to protect people's authorization and decide what permissions they have. In this paper, we used Pre-Trained CNN for extracts features from genuine and forged signatures, and three widely used classification algorithms, SVM (Support Vector Machine), NB (Naive Bayes) and KNN (k-nearest neighbors), these algorithms are compared to calculate the run time, classification error, classification loss and accuracy for test-set consist of signature images (genuine and forgery). Three classifiers have been applied using (UTSig) dataset; where run time, classification error, classification loss and accuracy were calculated for each classifier in the verification phase, the results showed that the SVM and KNN got the best accuracy (76.21), while the SVM got the best run time (0.13) result among other classifiers, therefore the SVM classifier got the best result among the other classifiers in terms of our measures.
关键词：CNN;Signature verification;SVM;KNN;NB