Off-line Signature Verification with concentric squares and slope based features using support vector machines

Author

Randhawa, M.K. ; Sharma, Arvind Kumar ; Sharma, Ratnesh K.

Author_Institution

Dept. of Comput. Sci., Guru Nanak Khalsa Coll., Karnal, India

fYear

2013

fDate

22-23 Feb. 2013

Firstpage

600

Lastpage

604

Abstract

An Off-line Signature Verification System (OSVS) with a novel feature extraction procedure has been described. Fusion of concentric squares having geometric features, zone based slope as well as slope angle have been considered as input patterns. The strong feature set thus obtained makes the OSVS accurate. Verification was performed by using Support Vector Machine (SVM) technique with different kernels. Empirically, Radial Basis Function (RBF) based SVM model exhibited the best results as compared to that based on linear and polynomial kernels. That is, the system attained False Acceptance Rate as 1.25% and False Rejection Rate as 1.66%.

Keywords

feature extraction; handwritten character recognition; radial basis function networks; set theory; support vector machines; OSVS; RBF-based SVM model; concentric square-based feature set; false acceptance rate; false rejection rate; feature extraction procedure; geometric features; input patterns; offline signature verification system; radial basis function-based SVM model; slope angle; support vector machine technique; zone-based slope features; Databases; Feature extraction; Handwriting recognition; Kernel; Polynomials; Support vector machines; Training; Concentric Squares; Geometric Features; Off-line Signature Verification; Slope; Slope Angle; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Advance Computing Conference (IACC), 2013 IEEE 3rd International

Conference_Location

Ghaziabad

Print_ISBN

978-1-4673-4527-9

Type

conf

DOI

10.1109/IAdCC.2013.6514295

Filename

6514295