3-D Offline Signature Verification with Convolutional Neural Network

Na Tyrer1, Fan Yang1, Gary C. Barber1, Guangzhi Qu1, Bo Pang1 and Bingxu Wang1, 2, 1Oakland University, USA, 2Zhejiang Sci-Tech University, China; Na Tyrer1, Fan Yang1, Gary C. Barber1, Guangzhi Qu1, Bo Pang1 and Bingxu Wang1, 2, 1Oakland University, USA, 2Zhejiang Sci-Tech University, China

3-D Offline Signature Verification with Convolutional Neural Network

Authors

Na Tyrer¹, Fan Yang¹, Gary C. Barber¹, Guangzhi Qu¹, Bo Pang¹ and Bingxu Wang^{1, 2}, ¹Oakland University, USA, ²Zhejiang Sci-Tech University, China

Abstract

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.

Keywords

Signature Verification, 3D Optical Profilometer, Convolutional Neural Network.

CS&IT Conference Proceedings

3-D Offline Signature Verification with Convolutional Neural Network