Offline Signature Recognition and Forgery Detection using Deep Learning

Abstract

Authentication plays a very important role to manage security. In the modern era, it is one, in all the priorities. With the appearance of technology, the interaction with machines is turning automatic. Therefore, the need of authentication increases rapidly for various security purposes. Because of this, the biometric-based authentication has gained a drastic momentum. It is a kind of boon over other techniques. However, this event is not a replacement of drawback but varied ways are adopted to verify folks. Signature is one of the first broadly practiced biometric features for the verification of an individual. This paper proposes a method for the pre-processing of signatures to make verification simple. It also proposed a novel method for signature recognition and signature forgery detection with verification using Convolution Neural Network (CNN), Crest-Trough method and SURF algorithm & Harris corner detection algorithm. The proposed system attains an accuracy of 85-89% for forgery detection and 90-94% for signature recognition.

Existing System

Discrete Wavelet Transform (DWT)

Proposed System

The contributions of the paper are as follows: Proposed preprocessing method to make verification of signature easier. Proposed CNN, Crest-Trough algorithm based model for Signature verification system. Proposed Harris, Surf based model for forgery detection in signature.

Architecture

BLOCK DIAGRAM