Off-Line Signature Verification Based on Directional Gradient Spectrum and a Fuzzy Classifier

2006 ◽  
pp. 1018-1029 ◽  
Author(s):  
Young Woon Woo ◽  
Soowhan Han ◽  
Kyung Shik Jang
Keyword(s):  
Signature Verification ◽  
Fuzzy Classifier

COMBINING EODH AND DIRECTIONAL GRADIENT DENSITY FOR OFFLINE SIGNATURE VERIFICATION

2009 ◽  
Vol 23 (06) ◽  
pp. 1161-1177 ◽  
Author(s):  
JING WEN ◽  
BIN FANG ◽  
Y. Y. TANG ◽  
PATRICK S. P. WANG ◽  
MIAO CHENG ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Feature Representation ◽  
Signature Verification ◽  
Fuzzy Classifier ◽  
Offline Signature Verification ◽  
Signature Database ◽  
Gradient Density ◽  
Full Capacity ◽  
Fusion Scheme ◽  
Distance Histogram

The main problem to identify skilled forgeries for offline signature verification lies in the fact that it is difficult to formalize distinguished feature representation of the signature patterns and design appropriate fusion scheme for various types of feature vectors. To tackle these problems, in this paper, we propose an approach to extract robust Edge Orientation Distance Histogram (EODH) descriptor which effectively reflects signature structure variations. In addition, directional gradient density features are employed for skilled forgery verification attempt. To exploit the full capacity of two sets of features, we designed the multilevel weighted fuzzy classifier and fuse match scores by way of selection priority. Experiments were conducted on a subcorpus of open MCYT signature database which is widely used for performance evaluation. It shows that the proposed method was able to improve verification accuracy.

scholarly journals Bagged ensemble of fuzzy classifiers and feature selection for handwritten signature verification

2019 ◽  
Vol 43 (5) ◽  
pp. 833-845 ◽  
Author(s):  
K.S. Sarin ◽  
I.A. Hodashinsky
Keyword(s):  
Feature Selection ◽  
Selection Procedure ◽  
Research Area ◽  
Signature Verification ◽  
Support Vector ◽  
Fuzzy Classifier ◽  
Data Set ◽  
Static Verification ◽  
Fuzzy Classifiers ◽  
Handwritten Signature

Handwritten signature verification is an important research area in the field of person authentication and biometric identification. There are two known methods for handwriting signature verification: if it is possible to digitize the speed of pen movement, then verification is said to be on-line or dynamic; otherwise, when only an image of handwriting is available, verification is said to be off-line or static. It is proved that when using dynamic verification, a greater accuracy is achieved than when using static verification. In the present work, the amplitudes, frequencies, and phases of the harmonics extracted from the signature signals of the X and Y coordinates of the pen movement using a discrete Fourier transform are used as characteristics of the signature. All signals are pre-processed in advance, including the elimination of gaps, the elimination of the angle of inclination, the normalization of position and scaling. A fuzzy classifier is proposed as a signature verification tool based on the features obtained. The work examines the effectiveness of this tool in the ensemble, as well as using a procedure for feature selection. To build an ensemble of classifiers, a well-known bagging method is used, and the feature selection is based on the determination of mutual information between a feature and a class of an object. Experiments on signature verification on the SVC2004 data set with the construction of a fuzzy classifier and ensembles of three, five, seven and nine fuzzy classifiers were conducted. Experiments were carried out both with the use of the feature selection procedure and without selection. The efficiency of the classifiers constructed is compared with each other and with known analogues: decision trees, support vector machines, discriminant analysis and k-nearest neighbors.

Improvement of Japanese Signature Verification by Combined Segmentation-verification Approach

2014 ◽  
Vol 134 (12) ◽  
pp. 1809-1816
Author(s):  
Yuta Kamihira ◽  
Wataru Ohyama ◽  
Tetsushi Wakabayashi ◽  
Fumitaka Kimura
Keyword(s):  
Signature Verification

Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images Using Neuro Fuzzy Classifier

2012 ◽  
Vol 58 (4) ◽  
pp. 425-431 ◽  
Author(s):  
D. Selvathi ◽  
N. Emimal ◽  
Henry Selvaraj
Keyword(s):  
Intravascular Ultrasound ◽  
Human Error ◽  
Human Life ◽  
Classification Performance ◽  
Atheromatous Plaque ◽  
Ultrasound Images ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Calcified Tissues

Abstract The medical imaging field has grown significantly in recent years and demands high accuracy since it deals with human life. The idea is to reduce human error as much as possible by assisting physicians and radiologists with some automatic techniques. The use of artificial intelligent techniques has shown great potential in this field. Hence, in this paper the neuro fuzzy classifier is applied for the automated characterization of atheromatous plaque to identify the fibrotic, lipidic and calcified tissues in Intravascular Ultrasound images (IVUS) which is designed using sixteen inputs, corresponds to sixteen pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is Fibrotic, Lipidic, Calcified or Normal pixel. The classification performance was evaluated in terms of sensitivity, specificity and accuracy and the results confirmed that the proposed system has potential in detecting the respective plaque with the average accuracy of 98.9%.

The Influence of Template Ageing on a Dynamic Signature Verification System

2014 ◽  
Vol 24 ◽  
pp. 47-52
Author(s):  
Joanna Putz-Leszczynska
Keyword(s):  
Potential Risk ◽  
Signature Verification ◽  
Verification System ◽  
Verification Systems ◽  
Dynamic Signature

This paper addresses template ageing in automatic signature verification systems. Handwritten signatures are a behavioral biometric sensitive to the passage of time. The experiments in this paper utilized a database that contains signature realizations captured in three sessions. The last session was captured seven years after the first one. The results presented in this paper show a potential risk of using an automatic handwriting verification system without including template ageing Purchase Article for $10 

A Review of Multi-Objective Evolutionary Based Fuzzy Classifiers

2020 ◽  
Vol 13 (1) ◽  
pp. 77-85
Author(s):  
Praveen Kumar Dwivedi ◽  
Surya Prakash Tripathi
Keyword(s):  
Fuzzy Systems ◽  
Evolutionary Process ◽  
Fuzzy Rule ◽  
Fuzzy Classification ◽  
Multi Objective Optimization ◽  
Fuzzy Classifier ◽  
Rule Based ◽  
Trade Off ◽  
Multi Objective ◽  
Rule Based Systems

Background: Fuzzy systems are employed in several fields like data processing, regression, pattern recognition, classification and management as a result of their characteristic of handling uncertainty and explaining the feature of the advanced system while not involving a particular mathematical model. Fuzzy rule-based systems (FRBS) or fuzzy rule-based classifiers (mainly designed for classification purpose) are primarily the fuzzy systems that consist of a group of fuzzy logical rules and these FRBS are unit annexes of ancient rule-based systems, containing the "If-then" rules. During the design of any fuzzy systems, there are two main objectives, interpretability and accuracy, which are conflicting with each another, i.e., improvement in any of those two options causes the decrement in another. This condition is termed as Interpretability –Accuracy Trade-off. To handle this condition, Multi-Objective Evolutionary Algorithms (MOEA) are often applied within the design of fuzzy systems. This paper reviews the approaches to the problem of developing fuzzy systems victimization evolutionary process Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off, current research trends and improvement in the design of fuzzy classifier using MOEA in the future scope of authors. Methods: The state-of-the-art review has been conducted for various fuzzy classifier designs, and their optimization is reviewed in terms of multi-objective. Results: This article reviews the different Multi-Objective Optimization (EMO) algorithms in the context of Interpretability -Accuracy tradeoff during fuzzy classification. Conclusion: The evolutionary multi-objective algorithms are being deployed in the development of fuzzy systems. Improvement in the design using these algorithms include issues like higher spatiality, exponentially inhabited solution, I-A tradeoff, interpretability quantification, and describing the ability of the system of the fuzzy domain, etc. The focus of the authors in future is to find out the best evolutionary algorithm of multi-objective nature with efficiency and robustness, which will be applicable for developing the optimized fuzzy system with more accuracy and higher interpretability. More concentration will be on the creation of new metrics or parameters for the measurement of interpretability of fuzzy systems and new processes or methods of EMO for handling I-A tradeoff.

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