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On Line Signature Verification Based On Forward And Backward Variances Of Signature

1915 words - 8 pages

Signature is one of behavioral characteristics of human that can be used to identify individual. Widely implemented in everyday life, signature has very high social acceptance \cite{jain1}. Signature mostly has been used to verify important documents, such as bank checks. However, in many occasions, human eyes cannot distinguish genuine from forgery signatures, that will become a serious problem in security system. Therefore, the necessity of high accuracy automatic signature verification systems is increasing nowadays.

Signature verification is divided into two classes based on the type of acquisition of signature , on-line and off-line signature verification \cite{impedovo1}. On-line signature verification uses digital input device, such as digital tablet and digital pen, to acquire data during signing process in the form of sampled signals. On the other hand, off-line signature verification performs static data acquisition using scanner. In this case, the data is represented as images.

The fluctuation of handwriting, the limited number of training data and the difficulty of extracting stable feature of signatures are some problems that must be faced in signature verification. Many works in on-line signature verification have been done in order to find the accurate method \cite{jain1,impedovo1,plamondon1}. Among those proposed methods, a lot of researchers have been used $x$ and $y$ positions of signatures directly. However we consider that it is not effective because genuine and forgery signatures have quite similar shape and they result the difficulty of the verification process. To overcome the problem, new signing features related to $x$ and $y$ positions of signature called forward and backward variances of signature are employed here. The experiments performed on public signature database MCYT-100 \cite{ortega1} are given, in order to show the effectiveness of the proposed signing features.

Many methods for on-line signature verification have used $x$ and $y$ components of pen-point movement to verify the signature. However, it is known that signatures signed by a particular person cannot be exactly the same even they were signed by the same person due to the fluctuation of handwriting in signing process. Therefore we consider that using the whole signature is not effective to verify the signer. Fig. \ref{fig:stablepoints} shows the five signatures signed by the same person. It can be seen from Fig. \ref{fig:stablepoints} that some part of the signatures have less fluctuation than the rest part of the handwriting. Those parts having less fluctuation in the signatures are considered as the stable features of handwriting in the signing process.
In this paper, such stable features are extracted by selecting particular time intervals corresponding to parts with less variations in signing process. The obtained stable features are used as unique features for signature verification.

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