18-07-2012, 12:24 PM
Off-line Signature Verification using Local Patterns
Off-line Signature Verification.doc (Size: 47.5 KB / Downloads: 56)
ABSTRACT:
Recently, several papers have proposed pseudo dynamic methods for automatic handwritten signature verification. Each of these papers uses texture measures of the gray level signature strokes. This paper explores the usefulness of local binary pattern (LBP) and local directional pattern (LDP) texture measures to discriminate off-line signatures. A comparison between several texture normalizations is made so as to look for reducing pen dependence. The experiments conducted with MCYT off-line and GPDS960Graysignature corpuses show that LDPs are more useful than LBPs for automatic verification of static signatures. Additionally, the results show that the LDP codes of the contour are more discriminating than the LDPs of the stroke interior, although their combination at score level improves the overall scheme performance. The results are obtained by modeling the signatures with a Support Vector Machine (SVM) trained with genuine samples and random forgeries, while random and simulated forgeries have been used for testing it.
Existing System:
Our existing system handwritten character recognition using Modified Direction Feature (MDF), it is nothing but a system which recognize a hand written character Modified Direction Feature (MDF) generated encouraging results, reaching an accuracy of 81.58%.
In this system each and every hand written character of a separate person is scanned and stored in database the scanned images are verified using MDF.
Disadvantage of the existing system
Accuracy of 81.58% is very less when compared to existing system
since each and every hand written character of a separate person is scanned and stored in database it is very time consuming and it takes more manpower
Since handwritten character recognition is not a most important identity of a human being this system is not widely used
Proposed system:
Our proposed system is Off-line Signature Verification using the Enhanced Modified Direction Feature and Neural-based Classification in which we are using MDF with signature images. Specifically, a number of features have been combined with MDF, to capture and investigated various structural and geometric properties of the signatures to perform verification or identification of a signature, several steps must be performed. After preprocessing all signatures from the database by converting them to portable bitmap (PBM) format, their boundaries are extracted to facilitate the extraction of features using MDF .Verification experiments are performed with classifiers We are using Radial Basis Function (RBF) which is a classifier which gives an accuracy level of 91.21%
Advantage of proposed system
Accuracy level of 91.21% which very high when compared to the existing system
It is very time saving
It is user friendly