Offline Signature Verification and Forgery Detection Based on Computer Vision and Fuzzy Logic

2014 ◽  
Vol 3 (4) ◽  
pp. 156
Author(s):  
Gautam S. Prakash ◽  
Shanu Sharma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Detection System ◽  
Structural Parameters ◽  
Signature Verification ◽  
Width Ratio ◽  
Forgery Detection ◽  
Offline Signature Verification ◽  
Artificial Neural

<p>Automated signature verification and forgery detection has many applications in the field of Bank-cheque processing,document  authentication, ATM access etc. Handwritten signatures have proved to be important in authenticating a person's identity, who is signing the document. In this paper a Fuzzy Logic and Artificial Neural Network Based Off-line Signature Verification and Forgery Detection System is presented. As there are unique and important variations in the feature elements of each signature, so in order to match a particular signature with the database, the structural parameters of the signatures along with the local variations in the signature characteristics are used. These characteristics have been used to train the artificial neural network. The system uses the features extracted from the signatures such as centroid, height – width ratio, total area, I<sup>st</sup> and II<sup>nd</sup> order derivatives, quadrant areas etc. After the verification of the signature the angle features are used in fuzzy logic based system for forgery detection.</p>

Signature Verification Using Artificial Neural Network

2018 ◽  
Vol 7 (12) ◽  
pp. 40
Author(s):  
Palak Patel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Signature Verification ◽  
Dynamic Information ◽  
Signature Recognition ◽  
Scanned Image ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Processing Techniques ◽  
Image Format

The human signature is most important for access. Signature of the person is important biometric attribute of a human being which is used to authenticate human identity. There are many biometric characteristics by which one can have own identity like face recognition, fingerprint detection, iris inspection and retina scanning. In non-vision based techniques voice recognition and signature verification are most widely used. Verification can be performed either Online or Offline. Online system of signature verification uses dynamic information of a signature captured at the time the signature is made. Offline system uses scanned image of signature. In this paper, I present a method for Offline Verification of signatures using a set of simple shape based geometric features. As signatures play an important role in financial, commercial and legal transactions, truly secured authentication becomes more and more crucial. This paper presents the off-line signature recognition & verification using neural network in which the human signature is captured and presented in the image format. Various image processing techniques are used to recognize and verify the signature. Preprocessing of a scanned image is necessary to isolate the signature part and to remove any spurious noise present. Initially system use database of signatures obtained from those individuals whose signatures have to be authenticated by the system. Then artificial neural network (ANN) is used to verify and classify the signatures. The implementation details and results are discussed in the paper.

SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK

1993 ◽  
Vol 07 (04) ◽  
pp. 669-688 ◽  
Author(s):  
JANE BROMLEY ◽  
JAMES W. BENTZ ◽  
LÉON BOTTOU ◽  
ISABELLE GUYON ◽  
YANN LECUN ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Delay ◽  
System Performance ◽  
Feature Vector ◽  
Signature Verification ◽  
The Novel ◽  
Verification Algorithm ◽  
Siamese Network ◽  
Artificial Neural

This paper describes the development of an algorithm for verification of signatures written on a touch-sensitive pad. The signature verification algorithm is based on an artificial neural network. The novel network presented here, called a “Siamese” time delay neural network, consists of two identical networks joined at their output. During training the network learns to measure the similarity between pairs of signatures. When used for verification, only one half of the Siamese network is evaluated. The output of this half network is the feature vector for the input signature. Verification consists of comparing this feature vector with a stored feature vector for the signer. Signatures closer than a chosen threshold to this stored representation are accepted, all other signatures are rejected as forgeries. System performance is illustrated with experiments performed in the laboratory.

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