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.

scholarly journals A Review - Signature Verification System Using Deep Learning: A Challenging Problem

2021 ◽  
pp. 295-298
Author(s):  
Punam R. Patil ◽  
Bhushan V. Patil
Keyword(s):  
Deep Learning ◽  
Signature Verification ◽  
Educational Institutions ◽  
Business Organizations ◽  
Verification Method ◽  
Offline Signature Verification ◽  
Online Signature ◽  
On Line ◽  
Online Signature Verification ◽  
Verification Process

One of the challenging and effective way of identifying person through biometric techniques is Signature verification as compared to traditional handcrafted system, where a forger has access and also attempt to imitate it which is used in commercial scenarios, like bank check payment, business organizations, educational institutions, government sectors, health care industry etc. so the signature verification process is used for human examination of a single known sample. There are mainly two types of signature verification: static and dynamic. i) Static or off-line verification is the process of verifying an electronic or document signature after it has been made, ii) Dynamic or on-line verification takes place as a person creates his/her signature on a digital tablet or a similar device. As compared, Offline signature verification is not efficient and slow for a large number of documents. Therefore although vast and extensive research on signature verification there is need to more focus and review on the online signature verification method to increase efficiency using deep learning.

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

Neural Networks on Handwritten Signature Verification

2011 ◽  
pp. 1232-1237
Author(s):  
J. Francisco Vargas ◽  
Miguel A. Ferrer
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Template Matching ◽  
Personal Identification ◽  
Signature Verification ◽  
Biometric Authentication ◽  
Cognitive Approach ◽  
Fractal Approach ◽  
Pattern Recognition Methods ◽  
Handwritten Signature

Biometric offers potential for automatic personal identification and verification, differently from other means for personal verification; biometric means are not based on the possession of anything (as cards) or the knowledge of some information (as passwords). There is considerable interest in biometric authentication based on automatic signature verification (ASV) systems because ASV has demonstrated to be superior to many other biometric authentication techniques e.g. finger prints or retinal patterns, which are reliable but much more intrusive and expensive. An ASV system is a system capable of efficiently addressing the task of make a decision whether a signature is genuine or forger. Numerous pattern recognition methods have been applied to signature verification. Among the methods that have been proposed for pattern recognition on ASV, two broad categories can be identified: memory-based and parameter-based methods as a neural network. The Major approaches to ASV systems are the template matching approach, spectrum approach, spectrum analysis approach, neural networks approach, cognitive approach and fractal approach. The proposed article reviews ASV techniques corresponding with approaches that have so far been proposed in the literature. An attempt is made to describe important techniques especially those involving ANNs and assess their performance based on published literature. The paper also discusses possible future areas for research using ASV.

FREEMAN CHAIN CODE AS REPRESENTATION IN OFFLINE SIGNATURE VERIFICATION SYSTEM

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Aini Najwa Azmi ◽  
Dewi Nasien ◽  
Azurah Abu Samah
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Rejection Rate ◽  
Data Representation ◽  
Signature Verification ◽  
Global Features ◽  
Chain Code ◽  
Verification System ◽  
Offline Signature Verification ◽  
Handwritten Signature

Over recent years, there has been an explosive growth of interest in the pattern recognition. For example, handwritten signature is one of human biometric that can be used in many areas in terms of access control and security. However, handwritten signature is not a uniform characteristic such as fingerprint, iris or vein. It may change to several factors; mood, environment and age. Signature Verification System (SVS) is a part of pattern recognition that can be a solution for such situation. The system can be decomposed into three stages: data acquisition and preprocessing, feature extraction and verification. This paper presents techniques for SVS that uses Freeman chain code (FCC) as data representation. In the first part of feature extraction stage, the FCC was extracted by using boundary-based style on the largest contiguous part of the signature images. The extracted FCC was divided into four, eight or sixteen equal parts. In the second part of feature extraction, six global features were calculated. Finally, verification utilized k-Nearest Neighbour (k-NN) to test the performance. MCYT bimodal database was used in every stage in the system. Based on our systems, the best result achieved was False Rejection Rate (FRR) 14.67%, False Acceptance Rate (FAR) 15.83% and Equal Error Rate (EER) 0.43% with shortest computation, 7.53 seconds and 47 numbers of features.

scholarly journals A STATIC SIGNATURE VERIFICATION SYSTEM BASED ON A COOPERATING NEURAL NETWORKS ARCHITECTURE

1994 ◽  
Vol 08 (03) ◽  
pp. 679-692 ◽  
Author(s):  
HUBERT CARDOT ◽  
MARINETTE REVENU ◽  
BERNARD VICTORRI ◽  
MARIE-JOSÈPHE REVILLET
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Rejection Rate ◽  
Acceptance Rate ◽  
Signature Verification ◽  
Geometrical Parameters ◽  
False Acceptance Rate ◽  
Static Information ◽  
Handwritten Signature ◽  
False Acceptance

We are applying neural networks to the problem of handwritten signature verification. Our system is working on checks, so we can only use the static information (the image). This static information is used in three representations: geometrical parameters, outline and image. Our system is composed of several neural networks which cooperate together during the learning and decision phases. The performances in generalization, obtained with a large-scale database of 6000 signatures from real checks on random forgeries, are False Acceptance Rate (FAR)=2% and False Rejection Rate (FRR)=4%.

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 Online Handwritten Signature Verification Using Neural Network Classifier Based on Principal Component Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Vahab Iranmanesh ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Wan Azizun Wan Adnan ◽  
Salman Yussof ◽  
Olasimbo Ayodeji Arigbabu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Rejection Rate ◽  
Component Analysis ◽  
Error Rates ◽  
Signature Verification ◽  
Feature Selection Technique ◽  
Online Signature ◽  
Online Signature Verification ◽  
Handwritten Signature

One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%.

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