Handwritten Signature Forgery Identification and Prediction using Harris Corner Detection and Siamese Network

Signature is a powerful identity of a person for various legal acts, to authorize a transactions to avoid financial crimes. Now-a-days, we are using touch-sensitive pad for the verification of the signatures. Thus there is high risk of forgery to happen due to replicate the signature patterns. So it is essential to detection and verify the signatures while using public platforms to avoid crime of accessing the personal data. The advancements in image processing and artificial neural network helps in identifying and validating the real signature with the fack ones. This paper proposed a signature validation process using harris corner detection and Siamese neural network. Harris corner detector helps to extract the corners and infer the features of an signature image, whereas Siamese network identifies the pattern of signature by learning with the help of similarity function by taking the probability of the signature images. The proposed model achieves better accuracy of 98% for the new signature identification to the original signatures.