Handwritten Signature Verification Based on Enhanced Direction and Grid Features

Abstract

Signatures continue to be an important biometric trait because it remains widely used primarily to authenticate the identity of human beings. This paper presents an efficient text-based directional signature recognition algorithm which verifies signatures, even when they are composed of special unconstrained cursive characters which are superimposed and embellished. This algorithm extends the character-based signature verification technique. The feature extraction techniques are optimized by implementing the proposed thinning technique that performs a repetitive scanning of the signature image and removes non-skeleton pixels until obtaining a structure in which all the pixels are skeletal ones. Further feature extraction enhancement is achieved by extending a new approach for fitting of a bounding rectangle to closed regions to compute a minimum bounding rectangle of any signature image. The computed minimum bounding rectangle is not necessarily horizontal. The minimum bounding rectangle is used to compute accurate geometric signature features such as true aspect ratio. The experiments carried out on the GPDS signature database and an additional database created from signatures captured using the ePadInk tablet, show that the approach is effective and efficient, with a positive verification rate of 96.56%.