Off-line Signature Verification Using HMM for Random, Simple and Skilled Forgeries
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15-10-2010, 09:46 AM

Prepared by:
Edson J. R. Justino
Flávio Bortolozzi
Robert Sabourin


The problem of signature verification is in theory a pattern recognition task used to discriminate two classes, original and forgery signatures. Even after many efforts in order to develop new verification techniques for static signature verification, the influence of the forgery types has not been extensively studied. This paper reports the contribution to signature verification considering different forgery types in an HMM framework. The experiments have shown that the error rates of the simple and random forgery signatures are very closed. This reflects the real applications in which the simple forgeries represent the principal fraudulent case. In addition, the experiments show promising results in skilled forgery verification by using simple static and pseudodinamic features.


In an off-line signature verification system, a signature
is acquired as an image. This image represents a
personal style of human handwriting, extensively
described by the graphometry. In such a system the
objective is to detect three types of forgeries, which are
related to intra and inter-personal variability. The first
type, called random forgery, is usually represented by a
signature sample that belongs to a different writer of the
signature model .The second one, called
simple forgery, is represented by a signature sample with
the same shape of the genuine writer’s name .
The last type is the skilled forgery, represented by a
suitable imitation of the genuine signature model .

Every type of forgery requests a different recognition
approach. Methods based on Static approach are usually
used to identify random and simple forgeries. The reason
is that these methods have shown to be more suitable to
describe characteristics related to the signature shape. For
this purpose, the graphometry-based approach has many
features that can be used, such as calibration, proportion,
guideline and base behaviors . In addition, other
features have been applied in this approach, like pixel
density , pixel distributions. However, static
features do not describe adequately the handwriting
motion. Therefore, it is not enough to detect skilled
A skilled forgery has almost the same shape of the
genuine signature. Therefore, It is more difficult to detect.
In this case, methods based on pseudodynamic approach
have shown to be more robust to identify this type of
forgery, since they are able to capture handwriting motion
details. The primitives related to the pseudodynamicbased
methods are usually derived from the graphometic
feature too, like axial slant and others.

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