Artificial Neural Network based Signature Recognition and Verification System
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Date
2013
Journal Title
Journal ISSN
Volume Title
Publisher
Uva Wellassa University of Sri Lanka
Abstract
The signature is an important biometric attribute of a person which can be used to authenticate
the personal identity because of its uniqueness for each person. Now a day the personal
signature has a significant value in day to day works. Because of its uniqueness there is a
certain pattern which can be identified by extracting certain unique features.
Though in present days signatures are using as the valid authentication mechanism, some
peoples are trying to imitate another person’s signature to achieve some narrow goals. It is also
common sight in Sri Lanka place like banks, government organizations, universities etc.
Therefore, it is essential to introduce a high accuracy validation mechanism for personal
authentication. The proposed system uses an efficient image processing and feature extraction
methods as well as well-trained neural network system.
Methodology
Implementation of the system mainly based on two phases, Training phase and recognition
phase. Several technologies, Programming languages and Libraries are used to design and
implement the system.
In the training phase there are several steps to be completed by the system before the training
get started. In pre-processing activities, system is mainly focusing on background elimination,
cropping (crop signature image according to the signature bounds), thresholding, thinning, and
image width transformation (Abikoye et al., 2011).
Feature extraction is one of the important parts of the system because powerful features directly
affect to the accuracy of the final output. In here firstly, system will collect some global features
such as pixel density, width to height ratio, maximum horizontal count and maximum vertical
count. Then system will extract some unique points based on vertical splitting and horizontal
splitting (Ashwiniet al., 2012). Those feature points are related with the image geometric centre
point. After the feature extraction process is completed and then system normalizes all the
features and added them to the input vector. Main purpose of the normalization is to convert
values into an acceptable range for the neural network (range between 0 and 1). Then, the
normalize data are used to train the Neural Network.
In the Recognition phase, all the steps up to feature normalization are accomplished and those
features are compared with trained Neural Network. Finally, the system will generate a unique
value and which reveals the acceptance and rejection of the relevant signature.
Description
Keywords
Computer Science, Science and Technology, Technology, System, Automation, Networks