scholarly journals Writer-independent Offline Handwritten Signature Verification using Novel Feature Extraction Techniques

2019 ◽  
Vol 177 (14) ◽  
pp. 21-27
Author(s):  
Md. Aminur ◽  
Sarker Miraz ◽  
S. M.
Keyword(s):  
Feature Extraction ◽  
Signature Verification ◽  
Extraction Techniques ◽  
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.

scholarly journals A Survey on Classification algorithms of Brain Images in Alzheimer’s disease based on Feature Extraction techniques

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ruhul Amin Hazarika ◽  
Arnab Kumar Maji ◽  
Samarendra Nath Sur ◽  
Babu Sena Paul ◽  
Debdatta Kandar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Classification Algorithms ◽  
Extraction Techniques ◽  
Brain Images

scholarly journals Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 114
Author(s):  
Tiziano Zarra ◽  
Mark Gino K. Galang ◽  
Florencio C. Ballesteros ◽  
Vincenzo Belgiorno ◽  
Vincenzo Naddeo
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Performance Optimization ◽  
Industrial Plant ◽  
Mathematical Treatment ◽  
Extraction Techniques ◽  
Peak Period ◽  
Linear Discriminant ◽  
Artificial Neural

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.

scholarly journals Comparison between different feature extraction techniques for audio-visual speech recognition

2007 ◽  
Vol 1 (1) ◽  
pp. 7-20 ◽  
Author(s):  
Alin G. Chiţu ◽  
Leon J. M. Rothkrantz ◽  
Pascal Wiggers ◽  
Jacek C. Wojdel
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Visual Speech ◽  
Extraction Techniques ◽  
Visual Speech Recognition
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