Robustness of Offline
Signature Verification Based on Gray Level Features
ABSTRACT:
Several papers
have recently appeared in the literature which propose pseudo-dynamic features
for automatic static handwritten signature verification based on the use of gray
level values from signature stroke pixels. Good results have been obtained
using rotation invariant uniform local binary patterns LBP plus LBP and
statistical measures from gray level co-occurrence matrices (GLCM) with MCYT
and GPDS offline signature corpuses. In these studies the corpuses contain
signatures written on a uniform white “nondistorting” background, however the
gray level distribution of signature strokes changes when it is written on a
complex background, such as a check or an invoice. The aim of this paper is to
measure gray level features robustness when it is distorted by a complex
background and also to propose more stable features. A set of different checks
and invoices with varying background complexity is blended with the MCYT and GPDS
signatures. The blending model is based on multiplication. The signature models
are trained with genuine signatures on white background and tested with other
genuine and forgeries mixed with different backgrounds. Results show that a basic
version of local binary patterns (LBP) or local derivative and directional
patterns are more robust than rotation invariant uniform LBP or GLCM features
to the gray level distortion when using a support vector machine with histogram
oriented kernels as a classifier.
EXISTING SYSTEM:
The verification by signature analysis
requires no invasive measurements and people are used to this event in their
day to day activities.
Two methods of signature verification
stand out. One is an offline method that uses an optical scanner to obtain
handwriting data from a signature written on paper. The other, which is
generally more successful, is an online method, which, with a special device,
measures the sequential data, such as handwriting speed and pen pressure.
Although less successful than the online method, offline systems do have a
significant advantage because they do not require access to special processing
systems when the signatures are produced
DISADVANTAGES OF EXISTING SYSTEM:
The corpuses
contain signatures written on a uniform white “nondistorting” background; however
the gray level distribution of signature strokes changes when it is written on
a complex background, such as a check or an invoice.
PROPOSED SYSTEM:
Among the techniques that analyze the
stroke thickness or stroke intensity variations, we highlight those that focus
on the gray level distribution in the signature stroke.
The aim of this paper is to evaluate the
dependence of the gray level based features and propose strategies to improve
their robustness to gray level distortion and segmentation errors due to
complex backgrounds.
ADVANTAGES OF PROPOSED SYSTEM:
The proposed
system measure gray level features robustness when it is distorted by a complex
background and also to propose more stable features. A set of different checks
and invoices with varying background complexity is blended
MODULES:
Authentication Module
Signature Submission
Module
Gray Level Features Module
Verification Module
Evaluation Module
MODULES DESCRIPTION:
Authentication Module
The first module of Robustness of Offline Signature
Verification Based on Gray Level Features is authentication. Authentication is
done to secure the application from unauthorized user. The username and
password is checked and the unauthorized user is ignored. The user can access
the application if the username and password is valid. As it is the first
module of the project it gives security to our application.
Signature Submission Module
Handwritten
signature is the result of a complex process depending on the psychophysical
state of the signer and the conditions under which the signing process occurs.
Although complex theories have been proposed to model the psychophysical mechanisms
underlying handwriting and the ink processes, signature verification is still
an open challenge. So in this module first we apply Pre processing.
Pre-processing is nothing but a process in which input is an image
the input image is converted into system readable format which is a
bitmap format And sent for further execution the purpose of converting it into
bitmap format is that in second module we are going extract the boundaries of
the signature if it is in bitmap format it would easy for the boundary
extraction.
Gray Level Features Module
Among the techniques
that analyze the stroke thickness or stroke intensity variations, we highlight
those that focus on the gray level distribution in the signature stroke. In
feature extraction the boundaries of the signature image is extracted using MDF
(modified extraction feature) for further modification purpose of extraction of
the signatures boundaries is that. It would be easy for the classifier to
identify and verify the signature because in the in the Feature extraction the
size of the image is reduced.
Verification Module
In the verification module, the input
signature is verified with the server authenticated signatures. And results
will be displayed based on the verification. An automatic signature verifier
should assess whether a questioned signature is an authentic signature normally
used by the reference writer. These parameters were
evaluated with different classifiers
such as nearest neighbor.
Evaluation Module:
In this evaluation module, we evaluate
the system with the compared signatures. Graph is plotted according to the
verified signatures. The experiments were designed to determine the influence
of the gray level distortion and segmentation errors on the verification task.
Therefore, the first experiment was aimed at showing the EER of different
verifier configurations (nearest neighbor classifier with histogram
intersection and Chi-square similarity measures and LS-SVM with linear, RBF,
histogram intersection and Chi-square kernels) with the different parameters
proposed.
HARDWARE
REQUIREMENTS
•
SYSTEM : Pentium IV 2.4 GHz
•
HARD
DISK : 40 GB
•
FLOPPY
DRIVE : 1.44 MB
•
MONITOR : 15 VGA colour
•
MOUSE : Logitech.
•
RAM : 256 MB
•
KEYBOARD :
110 keys enhanced.
SOFTWARE
REQUIREMENTS
•
Operating system :- Windows XP
Professional
•
Front End :- Microsoft Visual Studio .Net 2008
•
Coding Language : - C# .NET.
REFERENCE:
Miguel A. Ferrer, J. Francisco Vargas,
Aythami Morales, and Aarón Ordóñez, “Robustness of Offline Signature
Verification Based on Gray Level Features”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 7, NO. 3,
JUNE 2012.
