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 OFFLINE SIGNATURE VERIFICATION BASED ON GLCM

2014 ◽  
pp. 118-124
Author(s):  
P. NAGENDRA BABU ◽  
K.CHAITHANYA SAGAR ◽  
A.SURENDRA REDDY
Keyword(s):  
Local Binary Patterns ◽  
Signature Verification ◽  
Support Vector ◽  
White Background ◽  
Gray Level ◽  
Dynamic Features ◽  
Rotation Invariant ◽  
Statistical Measures ◽  
Complex Background ◽  
Handwritten Signature

Several papers have recently appeared in the literature which propose pseudo-dynamic features for automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels. Good results have been obtained using rotation invariant uniform local binary patternsLBP plus LBP and statistical measures from gray levelco-occurrence matrices (GLCM) with MCYT and GPDS offline signature corpuses. In these studies the corpuses contain signatures written on a uniform white “nondistorting” background, however the gray level distribution of signature strokes changes when it is written on a complex background, such as a check or an invoice. The aim of this paper is to measure gray level features robustness when it is distorted by a complex background and also to propose more stable features. A set of different checks and invoices with varying background complexity is blended with the MCYT and GPDS signatures. The blending model is based on multiplication. The signature models are trained with genuine signatures on white background and tested with other genuine and forgeries mixed with different backgrounds. Results show that a basic version of local binary patterns (LBP) or local derivative and directional patterns are more robust than rotation invariant uniform LBP or GLCM features to the gray level distortion when using a support vector machine with histogram oriented kernels as a classifier.

scholarly journals HANDWRITTEN SIGNATURE VERIFICATION BASED ON THE USE OF GRAY LEVEL VALUES

2013 ◽  
pp. 26-32
Author(s):  
P. RAMESH ◽  
P.NAGESWARA RAO
Keyword(s):  
Local Binary Patterns ◽  
Signature Verification ◽  
Support Vector ◽  
White Background ◽  
Gray Level ◽  
Dynamic Features ◽  
Rotation Invariant ◽  
Statistical Measures ◽  
Complex Background ◽  
Handwritten Signature

Recently several papers have appeared in the literature which propose pseudo-dynamic features for automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels. Good results have been obtained using rotation invariant uniform local binary patterns LBP plus LBP and statistical measures from gray level co-occurrence matrices (GLCM) with MCYT and GPDS offline signature corpuses. In these studies the corpuses contain signatures written on a uniform white “nondistorting” background, however the gray level distribution of signature strokes changes when it is written on a complex background, such as a check or an invoice. The aim of this paper is to measure gray level features robustness when it is distorted by a complex background and also to propose more stable features. A set of different checks and invoices with varying background complexity is blended with the MCYT and GPDS signatures. The blending model is based on multiplication. The signature models are trained with genuine signatures on white background and tested with other genuine and forgeries mixed with different backgrounds. Results show that a basic version of local binary patterns (LBP) or local derivative and directional patterns are more robust than rotation invariant uniform LBP or GLCM features to the gray level distortion when using a support vector machine with histogram oriented kernels as a classifier.

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 On-line Signature Verification Based on GA-SVM

2015 ◽  
Vol 11 (6) ◽  
pp. 49 ◽  
Author(s):  
Dong Huang ◽  
Jian Gao
Keyword(s):  
Genetic Algorithm ◽  
Feature Subset Selection ◽  
Signature Verification ◽  
Support Vector ◽  
Svm Classifier ◽  
Support Vector Data Description ◽  
Feature Subset ◽  
Dynamic Features ◽  
On Line ◽  
One Class Classifier

With the development of pen-based mobile device, on-line signature verification is gradually becoming a kind of important biometrics verification. This thesis proposes a method of verification of on-line handwritten signatures using both Support Vector Data Description (SVM) and Genetic Algorithm (GA). A 27-parameter feature set including shape and dynamic features is extracted from the on-line signatures data. The genuine signatures of each subject are treated as target data to train the SVM classifier. As a kernel based one-class classifier, SVM can accurately describe the feature distribution of the genuine signatures and detect the forgeries. To improving the performance of the authentication method, genetic algorithm (GA) is used to optimise classifier parameters and feature subset selection. Signature data form the SVC2013 database is used to carry out verification experiments. The proposed method can achieve an average Equal Error Rate (EER) of 4.93% of the skill forgery database.

scholarly journals Writer Independent Manipuri Offline Signature Verification using Transfer Learning Technique of Convolutional Neural Network

2019 ◽  
Vol 9 (1S) ◽  
pp. 76-80
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Learning Ability ◽  
Signature Verification ◽  
Support Vector ◽  
Svm Classifier ◽  
Powerful Learning ◽  
Offline Signature Verification ◽  
Learning Technique ◽  
Handwritten Signature

Automatic Signature Verification system is used to verify whether a signature is genuine or forged. Forged Signatures are those signatures that a person produced by imitating the signature of another person. Automatic Signature Verification is very important as a person’s handwritten signature is used everywhere to authenticate themselves and there is not very much difference between a genuine signature and the imitation of it, i.e. a forged signature. In this work, signature verification is done using different pre-trained Convolutional Neural Networks (CNNs). Convolutional Neural Network has powerful learning ability, and it can be used to distinguish between a genuine and a forged signature automatically. In this experiment, Manipuri signature dataset was used, the dataset was prepared originally and it contains 729 genuine signatures and 243 forged signatures. Features were extracted from pre-trained networks and classification was done using binary Support Vector Machine (SVM) classifier and the performances of the networks were compared. And according to the experiment we achieved a classification accuracy of 84.7 using VGG19 features, accuracy of 86.8 using VGG16 features and accuracy of 81.9 using Alexnet features.

SVM-DSmT COMBINATION FOR THE SIMULTANEOUS VERIFICATION OF OFF-LINE AND ON-LINE HANDWRITTEN SIGNATURES

2012 ◽  
Vol 11 (03) ◽  
pp. 1250018 ◽  
Author(s):  
NASSIM ABBAS ◽  
YOUCEF CHIBANI
Keyword(s):  
Function Theory ◽  
Belief Function ◽  
Signature Verification ◽  
Support Vector ◽  
Verification System ◽  
Belief Function Theory ◽  
On Line ◽  
Handwritten Signature ◽  
Belief Models ◽  
False Acceptance

A combination handwritten signature verification system is proposed for managing conflicts provided from each individual off-line and on-line support vector machine (SVM), respectively. Basically, the system is divided into three parts: (i) Off-line verification system, (ii) on-line verification system and (iii) combination module using belief function theory. The proposed framework allows combining the normalized SVM outputs and uses an estimation technique based on the dissonant model of Appriou to compute the belief assignments. Combination is performed using belief models such as Dempster-Shafer (DS) rule and proportional conflict redistribution (PCR) rule followed by the likelihood ratio-based decision making. Experiments are conducted on the well-known NISDCC signature collection using false rejection and false acceptance criteria. The obtained results show that the proposed combination framework using Dezert-Smarandache (DSm) theory yields the best verification accuracy even when individual off-line and on-line classifications provide conflicting results.

scholarly journals An Improved Geo-Textural Based Feature Extraction Vector For Offline Signature Verification

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Kennedy Gyimah ◽  
Justice Kwame Appati ◽  
Kwaku Darkwah ◽  
Kwabena Ansah
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Least Square Method ◽  
Kernel Functions ◽  
Least Square ◽  
Signature Verification ◽  
Support Vector ◽  
Offline Signature Verification ◽  
Occurrence Matrix ◽  
Handwritten Signature

In the field of pattern recognition, automatic handwritten signature verification is of the essence. The uniqueness of each person’s signature makes it a preferred choice of human biometrics. However, the unavoidable side-effect is that they can be misused to feign data authenticity. In this paper, we present an improved feature extraction vector for offline signature verification system by combining features of grey level occurrence matrix (GLCM) and properties of image regions. In evaluating the performance of the proposed scheme, the resultant feature vector is tested on a support vector machine (SVM) with varying kernel functions. However, to keep the parameters of the kernel functions optimized, the sequential minimal optimization (SMO) and the least square method was used. Results of the study explained that the radial basis function (RBF) coupled with SMO best support the improved featured vector proposed.

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