Signature verification is essential to prevent the forgery of documents in financial, commercial, and legal settings. There are many researchers have focused on this topic, however, utilizing the 3-D information presented by a signature using a 3D optical profilometer is a relatively new idea, and the convolutional neural network is a powerful tool for image recognition. The present research focused on using the 3 dimensions of offline signatures in combination with a convolutional neural network to verify signatures. It was found that the accuracy of the data for offline signature verification was over 90%, which shows promise for this method as a novel method in signature verification.
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