Manuscript Number : CSEIT1726283

An Offline Handwritten Signature Verification Using Low Level Stroke with Feature Extraction and Hybrid Classifiers

Authors(2) :-Nikhil Gupta, Dr. Rakesh Dhiman

Biometrics can be classified into two types, namely, physiological (fingerprint, Iris, face recognition etc) and behavioural (signature verification, keystroke dynamics etc.). In an authentication system, signature identification and verification plays an important role. Signature identification is again classified into two types, that is, static signature recognition (offline) and dynamic signature recognition (online).Online signature verification system uses a special sensor for capturing the image whereas in offline signature identification, no special sensor is required. Offline signature system needs only a pen and paper. Signature authentication is accepted as a legal mark of identification and authorization and finds an application in different fields like finance, bank and in jurisdictional documents. In this research work, we have proposed an offline signature verification system. Signature verification is a process in which a genuine person has been recognized on the basis of their signature. In the proposed work, signatures are executed by three processes like pre-processing, feature extraction and classification. In pre-processing, Binarization and color conversion has been performed. For extracting features, Low-level stroke feature technique along with SIFT method has been used. In the proposed work, we have used the combination of SVM and ANN as a classifier to classify the test data according to the training set. Initially, the features are trained using SVM, after that, the output of SVM act as the input of ANN and creates a better training structure to achieve better accuracy of proposed signature recognition system. The simulation is being performed in image processing toolbox under the MATLAB software. The performance metrics like FAR, FRR and Accuracy has been measured and comparison of proposed with existing technique has been provided.

Authors and Affiliations

Nikhil Gupta
Department of Information Technology Engineering,BGSB University, Rajouri, J & K, India
Dr. Rakesh Dhiman
Department of Computer science Engineering, NITTTR, Chandigarh, India

Biometrics, offline handwritten signature verification, Scale feature Transform (SIFT), Low level stroke, Support Vector Machine (SVM), Artificial Neural Network (ANN)

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Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1055-1061
Manuscript Number : CSEIT1726283
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Nikhil Gupta, Dr. Rakesh Dhiman, "An Offline Handwritten Signature Verification Using Low Level Stroke with Feature Extraction and Hybrid Classifiers", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.1055-1061, November-December-2017. |          | BibTeX | RIS | CSV

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