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Anova-based feature analysis and selection in HMM-based offline signature verification system

This paper presents an analysis performance of different features in distinguishing between genuine and forged signatures for HMM based offline signature verification systems. The four offline features include pixel density, centre of gravity, distance and angle. All features considered are local in...

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Bibliographic Details
Main Authors: Balbed M.A.M., Ahmad S.M.S., Shakil A.
Other Authors: 24721384800
Format: Conference Paper
Published: 2023
Subjects:
Hidden Markov models
Industrial applications
Intelligent systems
Pixels
Regression analysis
Analysis techniques
Center of gravity
Centre of gravity
Density features
Feature analysis
Off-line signature verification
Offline
Skilled forgery
Analysis of variance (ANOVA)
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Summary:This paper presents an analysis performance of different features in distinguishing between genuine and forged signatures for HMM based offline signature verification systems. The four offline features include pixel density, centre of gravity, distance and angle. All features considered are local in nature. The analysis technique used here is based on analysis of variance (ANOVA). Experimental results show that the combination of center of gravity and pixel density features are good for distinguishing between genuine and skilled forgeries for an HMM based offline signature verification system. � 2009 IEEE.

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