scholarly journals A Privacy-Preserving Handwritten Signature Verification Method Using Combinational Features and Secure KNN

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 46695-46705 ◽  
Author(s):  
Zhihua Xia ◽  
Tianjiao Shi ◽  
Neal N. Xiong ◽  
Xingming Sun ◽  
Byeungwoo Jeon
Keyword(s):  
Privacy Preserving ◽  
Signature Verification ◽  
Verification Method ◽  
Handwritten Signature

scholarly journals Handwritten Signature Verification Method based on Improved Combined Features

2021 ◽  
Vol 11 (13) ◽  
pp. 5867
Author(s):  
Yiwen Zhou ◽  
Jianbin Zheng ◽  
Huacheng Hu ◽  
Yizhen Wang
Keyword(s):  
Signature Verification ◽  
Support Vector ◽  
Dynamic Features ◽  
Local Data ◽  
Online Data ◽  
Verification Method ◽  
Combined Features ◽  
Score Fusion ◽  
Property Losses ◽  
Handwritten Signature

As a behavior feature, handwritten signatures are widely used in financial and administrative institutions. The appearance of forged signatures will cause great property losses to customers. This paper proposes a handwritten signature verification method based on improved combined features. According to advanced smart pen technology, when writing a signature, offline images and online data of the signature can be obtained in real time. It is the first time to realize the combination of offline and online. We extract the static and dynamic features of the signature and verify them with support vector machine (SVM) and dynamic time warping (DTW) respectively. We use a small number of samples during the training stage, which solves the problem of insufficient number of samples to a certain extent. We get two decision scores while getting the verification result. Finally, we propose a score fusion method based on accuracy (SF-A), which combines offline and online features through score fusion and utilize the complementarity among classifiers effectively. Experimental results show that using different numbers of training samples to conduct experiments on local data sets, the false acceptance rate (FAR) and false reject rate (FRR) obtained are better than the offline or online verification results.

scholarly journals An Offline Signature Verification and Forgery Detection Method Based on a Single Known Sample and an Explainable Deep Learning Approach

2020 ◽  
Vol 10 (11) ◽  
pp. 3716 ◽  
Author(s):  
Hsin-Hsiung Kao ◽  
Che-Yen Wen
Keyword(s):  
Deep Learning ◽  
Reference Sample ◽  
Rejection Rate ◽  
Personal Identification ◽  
Signature Verification ◽  
Verification Method ◽  
Testing Dataset ◽  
Verification Process ◽  
Local Feature Extraction ◽  
Handwritten Signature

Signature verification is one of the biometric techniques frequently used for personal identification. In many commercial scenarios, such as bank check payment, the signature verification process is based on human examination of a single known sample. Although there is extensive research on automatic signature verification, yet few attempts have been made to perform the verification based on a single reference sample. In this paper, we propose an off-line handwritten signature verification method based on an explainable deep learning method (deep convolutional neural network, DCNN) and unique local feature extraction approach. We use the open-source dataset, Document Analysis and Recognition (ICDAR) 2011 SigComp, to train our system and verify a questioned signature as genuine or a forgery. All samples used in our testing process are collected from a new author whose signatures are not present in the training or other stages. From the experimental results, we get the accuracy between 94.37% and 99.96%, false rejection rate (FRR) between 5.88% and 0%, false acceptance rate (FAR) between 0.22% and 5.34% in our testing dataset.

Handwritten Signature Verification Using Wrist-Worn Devices

2018 ◽  
Vol 2 (3) ◽  
pp. 1-26 ◽  
Author(s):  
Alona Levy ◽  
Ben Nassi ◽  
Yuval Elovici ◽  
Erez Shmueli
Keyword(s):  
Signature Verification ◽  
Handwritten Signature
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