An Automatic Off-Line Signature Verification and Forgery Detection System

2008 ◽  
pp. 63-89 ◽  
Author(s):  
Vamsi Krishna Madasu ◽  
Brian C. Lovell
Keyword(s):  
Membership Function ◽  
Detection System ◽  
Structural Parameters ◽  
Grid Method ◽  
Fuzzy Modeling ◽  
Signature Verification ◽  
Forgery Detection ◽  
Large Database ◽  
Factors Affecting ◽  
Handwritten Signature

This chapter presents an off-line signature verification and forgery detection system based on fuzzy modeling. The various handwritten signature characteristics and features are first studied and encapsulated to devise a robust verification system. The verification of genuine signatures and detection of forgeries is achieved via angle features extracted using a grid method. The derived features are fuzzified by an exponential membership function, which is modified to include two structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect other factors affecting the scripting of a signature. The efficacy of the proposed system is tested on a large database of signatures comprising more than 1,200 signature images obtained from 40 volunteers.

scholarly journals Adoption of Off-Line Signature Verification and Forgery Detection System Using Additive Fuzzy and TS Modelling Technique in Financial Auditing and Forensics Investigation

2021 ◽  
Vol 10 (6) ◽  
pp. 38-59
Author(s):  
Adeyemi B.M ◽  
Olaoye O.J ◽  
Uchehara C.C ◽  
Akinola O.M ◽  
Sunmola F.O
Keyword(s):  
Detection System ◽  
Structural Parameters ◽  
Grid Method ◽  
Financial Accounting ◽  
Signature Verification ◽  
Financial Fraud ◽  
Forensic Investigation ◽  
Forgery Detection ◽  
Tangible Assets ◽  
Proper Analysis

This paper presents a robust signature verification and forgery detection system using Additive fuzzy and TS modeling technique. The features of various handwritten signatures are sampled with proper analysis and encapsulated to devise an effective verification system. Grid method was used to extract features angles for detection of forgeries and verification of genuine signatures. In financial Accounting, Auditing and Forensic Investigation, signature forgery could occur in various ways. This could be carried out on papers, sales documents such as invoices or inventory procurement requisition paper, title documents on landed property or other tangible assets. It is also perpetrated on payment authorization such as cheques, payment vouchers both in cash and on bills. During this exercise, the fraud perpetrators perfect their concentration on the surface paper, and trace the original signature from the mandate given earlier. It has been difficult to use accounting and auditing professions to track down financial fraud in Nigeria mostly with the problem of unearthing ingenious fraud. Exponential membership function was used to fuzzified the derived functions, and modified into structural parameters suitable to adapt to any possible variations that may result from handwriting styles and also to reflect any other factors due to scripting of a signature. The proposed system is tested on a large database of signatures obtained from 40 subjects.

Offline Signature Verification and Forgery Detection Based on Computer Vision and Fuzzy Logic

2014 ◽  
Vol 3 (4) ◽  
pp. 156
Author(s):  
Gautam S. Prakash ◽  
Shanu Sharma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Detection System ◽  
Structural Parameters ◽  
Signature Verification ◽  
Width Ratio ◽  
Forgery Detection ◽  
Offline Signature Verification ◽  
Artificial Neural

<p>Automated signature verification and forgery detection has many applications in the field of Bank-cheque processing,document  authentication, ATM access etc. Handwritten signatures have proved to be important in authenticating a person's identity, who is signing the document. In this paper a Fuzzy Logic and Artificial Neural Network Based Off-line Signature Verification and Forgery Detection System is presented. As there are unique and important variations in the feature elements of each signature, so in order to match a particular signature with the database, the structural parameters of the signatures along with the local variations in the signature characteristics are used. These characteristics have been used to train the artificial neural network. The system uses the features extracted from the signatures such as centroid, height – width ratio, total area, I<sup>st</sup> and II<sup>nd</sup> order derivatives, quadrant areas etc. After the verification of the signature the angle features are used in fuzzy logic based system for forgery detection.</p>

Off-line signature verification and forgery detection using fuzzy modeling

2005 ◽  
Vol 38 (3) ◽  
pp. 341-356 ◽  
Author(s):  
Madasu Hanmandlu ◽  
Mohd. Hafizuddin Mohd. Yusof ◽  
Vamsi Krishna Madasu
Keyword(s):  
Fuzzy Modeling ◽  
Signature Verification ◽  
Forgery Detection

FORGERY DETECTION BY LOCAL CORRESPONDENCE

2001 ◽  
Vol 15 (04) ◽  
pp. 579-641 ◽  
Author(s):  
JINHONG KATHERINE GUO ◽  
DAVID DOERMANN ◽  
AZRIEL ROSENFELD
Keyword(s):  
Rapid Development ◽  
Personal Characteristics ◽  
Signature Verification ◽  
Forgery Detection ◽  
Good Correspondence ◽  
Value Differences ◽  
Global Statistics ◽  
The Cost ◽  
Online Verification ◽  
Interest In Research

Signatures may be stylish or unconventional and have many personal characteristics that are challenging to reproduce by anyone other than the original author. For this reason, signatures are used and accepted as proof of authorship or consent on personal checks, credit purchases and legal documents. Currently signatures are verified only informally in many environments, but the rapid development of computer technology has stimulated great interest in research on automated signature verification and forgery detection. In this paper, we focus on forgery detection of offline signatures. Although a great deal of work has been done on offline signature verification over the past two decades, the field is not as mature as online verification. Temporal information used in online verification is not available offline and the subtle details necessary for offline verification are embedded at the stroke level and are hard to recover robustly. We approach the offline problem by establishing a local correspondence between a model and a questioned signature. The questioned signature is segmented into consecutive stroke segments that are matched to the stroke segments of the model. The cost of the match is determined by comparing a set of geometric properties of the corresponding substrokes and computing a weighted sum of the property value differences. The least invariant features of the least invariant substrokes are given the biggest weights, thus emphasizing features that are highly writer-dependent. Random forgeries are detected when a good correspondence cannot be found, i.e. the process of making the correspondence yields a high cost. Many simple forgeries can also be identified in this way. The threshold for making these decisions is determined by a Gaussian statistical model. Using the local correspondence between the model and a questioned signature, we perform skilled forgery detection by examining the writer-dependent information embedded at the substroke level and try to capture unballistic motion and tremor information in each stroke segment, rather than as global statistics. Experiments on random, simple and skilled forgery detection are presented.

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