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signature verification project source code and documentation

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.