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.

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.

Robust Offline Signature Verification Based On Global Features

2009 ◽  
Author(s):  
A.C. Ramachandra ◽  
Jyothi Srinivasa Rao ◽  
K B Raja ◽  
K R Venugopla ◽  
L M Patnaik
Keyword(s):  
Signature Verification ◽  
Global Features ◽  
Offline Signature Verification

scholarly journals Offline Signature Verification System with Gaussian Mixture Models (GMM)

2013 ◽  
Vol 10 (6) ◽  
pp. 1700-1705
Author(s):  
Charu Jain ◽  
Priti Singh ◽  
Preeti Rana
Keyword(s):  
Mixture Models ◽  
Reference Model ◽  
Gaussian Mixture Models ◽  
Rejection Rate ◽  
Gaussian Mixture ◽  
Signature Verification ◽  
Probability Match ◽  
Final Decision ◽  
Global Features ◽  
The Individual

Gaussian Mixture Models (GMMs) has been proposed for off-line signature verification. The individual Gaussian components are shown to represent some global features such as skewness, kurtosis, etc. that characterize various aspects of a signature, and are effective for modeling its specificity. The learning phase involves the use of Gaussian Mixture Model (GMM) technique to build a reference model for each signature sample of a particular user. The verification phase uses three layers of statistical techniques. The first layer involves computation of GMM-based log-likelihood probability match score,  second layer performs the mapping of this score into soft boundary ranges of acceptance or rejection through the use of z-score analysis and normalization function, thirdly, threshold is used to arrive at the final decision of accepting or rejecting a given signature sample. The focus of this work is on faster detection of authenticated signature as no vector analysis is done in GMM. From the experimental results, the new features proved to be more robust than other related features used in the earlier systems. The FAR (False Acceptance Rate) and FRR (False Rejection Rate) for the genuine samples is 0.15 and 0.19 respectively.

A new wrapper feature selection method for language-invariant offline signature verification

2021 ◽  
pp. 115756
Author(s):  
Debanshu Banerjee ◽  
Bitanu Chatterjee ◽  
Pratik Bhowal ◽  
Trinav Bhattacharyya ◽  
Samir Malakar ◽  
...  
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Signature Verification ◽  
Offline Signature Verification ◽  
Wrapper Feature Selection

A framework for offline signature verification system: Best features selection approach

2020 ◽  
Vol 139 ◽  
pp. 50-59 ◽  
Author(s):  
Muhammad Sharif ◽  
Muhammad Attique Khan ◽  
Muhammad Faisal ◽  
Mussarat Yasmin ◽  
Steven Lawrence Fernandes
Keyword(s):  
Signature Verification ◽  
Features Selection ◽  
Verification System ◽  
Offline Signature Verification ◽  
Selection Approach

scholarly journals Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2910
Author(s):  
Kei Suzuki ◽  
Tipporn Laohakangvalvit ◽  
Ryota Matsubara ◽  
Midori Sugaya
Keyword(s):  
Feature Selection ◽  
Single Channel ◽  
Feature Selection Method ◽  
Classification Model ◽  
Features Selection ◽  
Selection Methods ◽  
Emotion Classification ◽  
Model Based ◽  
Physiological Indexes ◽  
Emotion Estimation

In human emotion estimation using an electroencephalogram (EEG) and heart rate variability (HRV), there are two main issues as far as we know. The first is that measurement devices for physiological signals are expensive and not easy to wear. The second is that unnecessary physiological indexes have not been removed, which is likely to decrease the accuracy of machine learning models. In this study, we used single-channel EEG sensor and photoplethysmography (PPG) sensor, which are inexpensive and easy to wear. We collected data from 25 participants (18 males and 7 females) and used a deep learning algorithm to construct an emotion classification model based on Arousal–Valence space using several feature combinations obtained from physiological indexes selected based on our criteria including our proposed feature selection methods. We then performed accuracy verification, applying a stratified 10-fold cross-validation method to the constructed models. The results showed that model accuracies are as high as 90% to 99% by applying the features selection methods we proposed, which suggests that a small number of physiological indexes, even from inexpensive sensors, can be used to construct an accurate emotion classification model if an appropriate feature selection method is applied. Our research results contribute to the improvement of an emotion classification model with a higher accuracy, less cost, and that is less time consuming, which has the potential to be further applied to various areas of applications.

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