A comparative study of the recent trends in biometric signature verification

Author

Zareen, Farhana Javed ; Jabin, Suraiya

Author_Institution

Dept. of Comput. Sci., Jamia Millia Islamia, New Delhi, India

fYear

2013

fDate

8-10 Aug. 2013

Firstpage

354

Lastpage

358

Abstract

Signature verification is a widely and commonly accepted practice for authentication of an individual. Whereas off-line signature verification contributes very less to accurate identification, on-line signature verification has been successfully implemented in recent researches to achieve 80%-98% of accuracy. Various approaches have been used to implement biometric signature recognition some of which are dynamic time warping (DTW), Bayesian Learning, Hidden Markov model (HMM), Neural Networks, Support Vector machine (SVM) etc. This paper presents a comparative and qualitative study of these methods used for biometric signature verification.

Keywords

belief networks; handwriting recognition; hidden Markov models; learning (artificial intelligence); neural nets; support vector machines; Bayesian learning approach; DTW approach; HMM approach; biometric signature verification; dynamic time warping approach; hidden Markov model approach; neural networks approach; offline signature verification; online signature verification; support vector machine approach; Artificial neural networks; Bayes methods; Handwriting recognition; Hidden Markov models; Support vector machines; Bayesian Learning; Biometric; DTW; HMM; Neural Networks; SVM; Signature Verification;

fLanguage

English

Publisher

ieee

Conference_Titel

Contemporary Computing (IC3), 2013 Sixth International Conference on

Conference_Location

Noida

Print_ISBN

978-1-4799-0190-6

Type

conf

DOI

10.1109/IC3.2013.6612219

Filename

6612219