文章基本信息

标题：Offline Signature Recognition via Convolutional Neural Network and Multiple Classifiers
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作者：Fadi Mohammad Alsuhimat ; Fatma Susilawati Mohamad
期刊名称：International Journal of Network Security & Its Applications
印刷版ISSN：0975-2307
电子版ISSN：0974-9330
出版年度：2022
卷号：14
期号：1
页码：43-52
语种：English
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：One of the most important processes used by companies to safeguard the security of information and prevent it from unauthorized access or penetration is the signature process. As businesses and individuals move into the digital age, a computerized system that can discern between genuine and faked signatures is crucial for protecting people's authorization and determining 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：Offline Signature Recognition;CNN;SVM;KNN;NB