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Advanced Signature Verification Techniques: A Review

Shivam Sharma, Yash Jain, Yagya Bharadwaj, Deepak Moud


There are various authentication techniques existing in these days to verify the originality of the owner’s identification, based on new technology and human computer interfaces like voice recognition and image processing like face detection methods to avoid the frauds. The popular noncomputer vision-based techniques like fingerprint authentication and passwords are most popular now, but what about the traditional method of the authenticity i.e., handwritten signature. In this era of technology and computer vision, the handwritten signatures are still the most popular way to represent a person’s identity and verification, it is like a “Seal of verification and authentication”. This research work is a study of advanced signature verification system as in this era it is very complicated to avoid vulnerabilities in such a system. This research work is a study of the signature verification system based on Artificial Neural Networks (ANN) that gave the confidence of a given signature if it is forged or not. In this study, a solution based on Convolutional Neural Networks (CNN) is represented which gives the result that whether the given signature is forged or original.


Offline signature verification, FAR (False Acceptance Rate), FRR (False Rejection Rate), CNN (Convolutional Neural Network)

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