An enhanced handwritten signature verification model applied on off-line benchmark data sets

ABSTRACT

The automatic, highly accurate and instantaneous authentication of a static off-line handwritten signature is highly required in financial, legal and governmental sectors. The genuine signature features are extracted by models like scale-invariant feature transform (SIFT) and speeded-up robust features (SURF), then the common patterns among these features are recognised for verifying new signatures. Although SIFT/SURF-based matching models are commonly used because of their robustness and low computational complexity, their tolerance to the unconstrained signature styles of large variations is not very satisfactory. This work presents three main drawbacks of the traditional application of this model in signature verification and proposes a set of modifications to customise this model to fit the nature of the handwriting domain. These modifications consider initially the limited size of the descriptor window in SIFT/SURF model, then consider the single point-to-point and the crisp matching of the points-of-interest in the tested signature pairs. The experimental work is applied on two benchmark data sets that contain a set of genuine and skilled forged signatures of multiple users. Furthermore, a comparative analysis is applied to show the reflection of each modification in enhancing the accuracy percentage of the signature verification.

KEYWORDS:

• Signature authentication
• handwritten signature
• feature extraction
• similarity detection