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.

FEATURE SELECTION METHOD FOR OFFLINE SIGNATURE VERIFICATION

2015 ◽  
Vol 75 (4) ◽  
Author(s):  
Zuraidasahana Zulkarnain ◽  
Mohd Shafry Mohd Rahim ◽  
Nur Zuraifah Syazrah Othman
Keyword(s):  
Selection Process ◽  
Feature Selection Method ◽  
Rejection Rate ◽  
Complex Problem ◽  
Signature Verification ◽  
Horizontal Distance ◽  
Features Selection ◽  
Global Features ◽  
Offline Signature Verification ◽  
The Difference

Signature verification is defined as one of the biometric identification method using a person’s signature characteristics. The task of verifying the genuineness of a person signature is a complex problem due to the inconsistencies in the person signatures such as slant, strokes, alignment, etc. Too many features may decrease the False Rejection Rate (FRR) but also increases the False Acceptance Rate (FAR). A low value of FAR and FRR are required to obtain accurate verification result. There is a need to select the best features set of the signatures attributes among them. A combination of the current global features with four new features will be proposed such as horizontal distance, vertical distance, hypotenuse distance and angle. However, the value of FAR may increase if too many features are used which result a slow verification performance. In order to select the best features, the difference between the mean of the standard deviation ratio of each feature will be used. The main objective is to increase the accuracy of verification rate. This can be determined using best features set selected during the features selection process. A selection of signature set with strong feature sets will be used as a control parameter. The parameter is then used to validate the 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.

scholarly journals Offline Signature Verification System Using SVM Classifier with Image Pre-processing Steps and SURF Algorithm

2021 ◽  
Vol 2107 (1) ◽  
pp. 012069
Author(s):  
Li Wen Goon ◽  
Swee Kheng Eng
Keyword(s):  
Feature Extraction ◽  
Noise Removal ◽  
Signature Verification ◽  
Support Vector ◽  
Svm Classifier ◽  
Speeded Up Robust Features ◽  
Verification System ◽  
Offline Signature Verification ◽  
Average Accuracy ◽  
Processing Steps

Abstract A signature is a mark or name that represents the identity of the people and the Signature Verification System (SVS) is used to validate the identity of people. The signature verification system is mostly used for bank cheques, vouchers, intelligence agencies and others. There are two types of SVS which are online and offline signature verification systems. The paper deals with an offline signature verification system. The proposed system consists of four main stages, (i) image acquisition, (ii) image pre-processing, (iii) feature extraction and (iv) classification. The image pre-processing steps involved binarization, noise removal using Gaussian filter and image resizing and thinning. In the feature extraction stage, Bag-of-Features with the Speeded Up Robust Features (SURF) extractor was utilized. In the third stage, the Support Vector Machine (SVM) classifier is used. Lastly, the confusion matrix and the verification rate were used to evaluate the performance of the classifier. In this paper, we implement and compare the performance of the signature verification system without entering the user ID and the signature verification system entering the user ID. For the ratio of 75% and 25% of the training and testing, respectively, the average accuracy for the signature verification system without entering the user ID is 71.36%, whereas the average accuracy for the signature verification system entering the user ID is 79.55%.

scholarly journals Offline Signature Identification and Verification Based on Capsule Representations

2020 ◽  
Vol 20 (5) ◽  
pp. 60-67
Author(s):  
Dilara Gumusbas ◽  
Tulay Yildirim
Keyword(s):  
Feature Extraction ◽  
Daily Life ◽  
Extraction Methods ◽  
Signature Verification ◽  
Verification Task ◽  
Feature Representations ◽  
Automatic Feature Extraction ◽  
Offline Signature Verification ◽  
Orientation Information ◽  
Trained Network

AbstractOffline signature is one of the frequently used biometric traits in daily life and yet skilled forgeries are posing a great challenge for offline signature verification. To differentiate forgeries, a variety of research has been conducted on hand-crafted feature extraction methods until now. However, these methods have recently been set aside for automatic feature extraction methods such as Convolutional Neural Networks (CNN). Although these CNN-based algorithms often achieve satisfying results, they require either many samples in training or pre-trained network weights. Recently, Capsule Network has been proposed to model with fewer data by using the advantage of convolutional layers for automatic feature extraction. Moreover, feature representations are obtained as vectors instead of scalar activation values in CNN to keep orientation information. Since signature samples per user are limited and feature orientations in signature samples are highly informative, this paper first aims to evaluate the capability of Capsule Network for signature identification tasks on three benchmark databases. Capsule Network achieves 97 96, 94 89, 95 and 91% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 and 32×32 resolutions, which are lower than usual, respectively. The second aim of the paper is to generalize the capability of Capsule Network concerning the verification task. Capsule Network achieves average 91, 86, and 89% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 resolutions, respectively. Through this evaluation, the capability of Capsule Network is shown for offline verification and identification tasks.

Sign in / Sign up

Export Citation Format

Share Document