Offline Signature Verification and Recognition System
Rosy Vig, Mahesh Kumar · 2014
Authentication of a person is the major concern in this era for security purposes. A number of biometric techniques have been proposed for personal identification in the past. Among the vision based ones are face recognition, fingerprint recognition, iris scanning and retina scanning. The primary advantage that signature verification systems have over other type's technologies is that signatures are already accepted as the common method of identity verification. As signatures continue to play an important role in financial, commercial and legal transactions, truly secured authentication becomes more and more crucial. A signature by an authorized person is considered to be the seal of approval and remains the most preferred means of authentication. The method presented in this paper consists of image prepossessing, geometric feature extraction, neural network training and verification. In this paper signatures from database are extracted before feature extraction. After the signature is scanned through device and entered in to the system, it then be converted to gray image representing black and white in two dimensional image. A verification stage includes applying the extracted features of test signature to a trained neural network which will classify it as genuine or forged. The signature verification system is designed using MATLAB. These extracted features are then applied as input to a trained neural network which will classify it as a genuine or forged signature.