scholarly journals Feature Extraction for Handwritten Signature Verification

2019 ◽  
Author(s):  
Anastasia Beresneva ◽  
Anna Epishkina
Keyword(s):  
Feature Extraction ◽  
Signature Verification ◽  
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.

scholarly journals SIGNATURE IDENTIFICATION AND VERIFICATION SYSTEMS: A COMPARATIVE STUDY ON THE ONLINE AND OFFLINE TECHNIQUES

2020 ◽  
Vol 5 (1) ◽  
pp. 28-45
Author(s):  
Nehal Hamdy Al-banhawy ◽  
◽  
Heba Mohsen ◽  
Neveen Ghali ◽  
◽  
...  
Keyword(s):  
Feature Extraction ◽  
Comparative Study ◽  
Active Area ◽  
Signature Verification ◽  
Classification Techniques ◽  
Verification Systems ◽  
Identification Techniques ◽  
Handwritten Signature ◽  
Verification Techniques

Handwritten signature identification and verification has become an active area of research in recent years. Handwritten signature identification systems are used for identifying the user among all users enrolled in the system while handwritten signature verification systems are used for authenticating a user by comparing a specific signature with his signature that is stored in the system. This paper presents a review for commonly used methods for pre-processing, feature extraction and classification techniques in signature identification and verification systems, in addition to a comparison between the systems implemented in the literature for identification techniques and verification techniques in online and offline systems with taking into consideration the datasets used and results for each system

scholarly journals Offline Signature Verification using Locally Optimized Distance-based Classification

2013 ◽  
Vol 50 ◽  
Author(s):  
Yaseen Moolla ◽  
Serestina Viriri ◽  
Fulufhelo Nelwamondo ◽  
Jules-Raymond Tapamo
Keyword(s):  
Feature Extraction ◽  
Research Work ◽  
Directional Pattern ◽  
Signature Verification ◽  
Extraction Technique ◽  
Classification Techniques ◽  
Local Directional Pattern ◽  
Handwritten Signature ◽  
Verification Techniques ◽  
Feature Extraction Technique

Although handwritten signature verification has been extensively researched, it has not achieved an optimal classification accuracy rate. Therefore, efficient and accurate signature verification techniques are required since signatures are still widely used as a means of personal verification. This research work presents efficient distance-based classification techniques as an alternative to supervised learning classification techniques (SLTs). The Local Directional Pattern (LDP) feature extraction technique was used to analyze the effect of using several different distance-based classification techniques. The classification techniques tested, are the Euclidean, Manhattan, Fractional, weighted Euclidean, weighted Manhattan, weighted fractional distances and individually optimized resampling of feature vector sizes. The best accuracy, of 90.8%, was achieved through applying a combination of the weighted fractional distances and locally optimized resampling classification techniques to the Local Directional Pattern feature extraction. These results are compared with results from literature, where the same feature extraction technique was classified with SLTs. The distance-based classification was found to produce greater accuracy than the SLTs.

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.

Handwritten Signature Verification with 2D Color Barcodes

2014 ◽  
Author(s):  
Marco Querini ◽  
Marco Gattelli ◽  
Valerio M. Gentile ◽  
Giuseppe F. Italiano
Keyword(s):  
Signature Verification ◽  
Handwritten Signature

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

Handwritten Signature Verification via Deep Sparse Coding Architecture

2018 ◽  
Author(s):  
Dimitros Tsourounis ◽  
Ilias Theodorakopoulos ◽  
Elias N. Zois ◽  
George Economou ◽  
Spiros Fotopoulos
Keyword(s):  
Sparse Coding ◽  
Signature Verification ◽  
Handwritten Signature
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