INTELLEGENT APPROACH FOR OFFLINE SIGNATURE VERIFICATION USING CHAINCODE AND ENERGY FEATURE EXTRACTION ON MULTICORE PROCESSOR

The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identification. A number of biometric techniques have been proposed for personal identification in the past. Among the vision-based ones are voice recognition, iris scanning and retina scanning, fingerprint recognition, face recognition. Signature verification are the most widely known among the non-vision based ones. As signatures continue to play a very important role in financial, legal transactions and commercial, in truly secured authentication becomes more and more crucial. However human signatures can be handled as an image and recognized using computer vision and neural network techniques. There are various approaches to signature recognition with a lot of scope of research. In this paper consists of image prepossessing, parallel process, feature extraction, verification and neural network training with extracted features. A verification stage includes applying the extracted features of test signature to a trained neural network which will classify it as a genuine or forged. In this paper, offline signature recognition & verification using neural network is proposed. Signatures are verified based on parameters extracted from the signature using parallel processing techniques.