Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks
Shashi Kumar, K. Bommanna Raja, R.K. Chhotaray, Sabyasachi Pattanaik · 2010
Signature is widely used and developed area of research for personal verification and authentication. In this paper Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks (SVFGNN) is presented. The global and grid features are fused to generate set of features for the verification of signature. The test signature is compared with data base signatures based on the set of features and match/non match of signatures is decided with the help of Neural Network. The performance analysis is conducted on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm. Biometrics is the science of automatic recognition of individual depending on their physiological and behavioral attributes. The expansion of networked society and increased use of some personal portable devices like tablet PCs, PDAs, mobile phones and authorization of access to sensitive data, is demanding the most reliable personal identification and authentication systems. Among the different forms of biometric recognition systems such as fingerprint, iris, face, voice, palm etc., signature will be most widely used. The applications like government and legal financial transaction, bank cheques use signature as one of the personal identification system. The financial transactions and shopping using debit cards and credit cards require a bill to be confirmed by handwritten signature. But this leads to increased risk of financial loss due to attempted forgeries. This problem may be resolved by introducing automatic recognition systems which are being successfully used effectively to analyse large quantities of biometric data. Since olden days handwritten signature has been most widely used and accepted individual attributes for recognition. The design and development of signature recognition system is really big challenge because of the increased dependence of personal identification systems. Signature recognition system is divided into On-line or dynamic and off-line or static recognition. On-line recognition refers to a process where the signer uses a special pen called stylus to create his or her signature, producing the pen locations, speed and pressure, where as off-line recognition deals with signature images acquired by a scanner or a digital camera. In general, off-line signature recognition is a challenging problem, unlike the on-line signature where dynamic aspects of the signing action are captured directly as the handwriting trajectory. Contribution: In this paper, the grid and global features of signature are fused to generate final feature vector of signature. The Neural Network (NN) is used as a classifier for the verification of signatures.