State-of-the-Art in Handwritten Signature Verification System

2015 ◽  
Author(s):  
Rania A. Mohammed ◽  
Rebaz M. Nabi ◽  
Sardasht M-Raouf Mahmood ◽  
Rebwar M. Nabi
Keyword(s):  
State Of The Art ◽  
Signature Verification ◽  
Verification System ◽  
Handwritten Signature

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.

Novel handwritten signature verification system based on shadow sensing

2017 ◽  
Author(s):  
Dibyendu Roy ◽  
Arijit Chowdhury ◽  
Arijit Sihnaray ◽  
Avik Ghose
Keyword(s):  
Signature Verification ◽  
Verification System ◽  
Handwritten Signature

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.

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