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Reseach Article

Multimodal Biometric System Using Speech and Signature Modalities

by Manvjeet Kaur, Akshay Girdhar, Mandeep Kaur
journal cover thumbnail
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 12
Year of Publication: 2010
Authors: Manvjeet Kaur, Akshay Girdhar, Mandeep Kaur
10.5120/962-1339

Manvjeet Kaur, Akshay Girdhar, Mandeep Kaur . Multimodal Biometric System Using Speech and Signature Modalities. International Journal of Computer Applications. 5, 12 ( August 2010), 13-16. DOI=10.5120/962-1339

@article{ 10.5120/962-1339,
author = { Manvjeet Kaur, Akshay Girdhar, Mandeep Kaur },
title = { Multimodal Biometric System Using Speech and Signature Modalities },
journal = { International Journal of Computer Applications },
issue_date = { August 2010 },
volume = { 5 },
number = { 12 },
month = { August },
year = { 2010 },
issn = { 0975-8887 },
pages = { 13-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume5/number12/962-1339/ },
doi = { 10.5120/962-1339 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:54:09.208346+05:30
%A Manvjeet Kaur
%A Akshay Girdhar
%A Mandeep Kaur
%T Multimodal Biometric System Using Speech and Signature Modalities
%J International Journal of Computer Applications
%@ 0975-8887
%V 5
%N 12
%P 13-16
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper discusses about Multimodal Biometric System which are used to overcome some of the problems of unimodal systems like noise in sensed data, intra-class variations, distinctiveness, and spoof attacks. Multimodal biometrics is the combination of two or more modalities such as signature and speech modalities. In this work an online signature verification system and speaker verification system are combined as these modalities are widely accepted and natural to produce. Although this combination of multimodal enhances security and accuracy, yet the complexity of the system increases due to increased number of features extracted out of the multiple samples and suffers from additional cost in terms of acquisition time. So these days the key issue is at what degree features are to be extracted and how the cost factor can be minimized, as the number of features increases the variability of the intra-personal samples due to greater lag times in between consecutive acquisitions of the sample also increases. Increase in variability of the system will further increase FAR. Thus to resolve these issues an effective fusion level and fusion mode is required. This paper presents a novel user authentication system based on a combined acquisition of online pen and speech signals.

References
  1. Anil K. Jain, Arun Ross and Salil Prabhakar, “An Introduction to Biometric Recognition”, IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image- and Video-Based Biometrics,Vol. 14, No. 1, pp.1782-1793, 2004.
  2. Brad Ulery, William Fellner, Peter Hallinan, Austin Hicklin, Craig Watson and Mitretek Systems, “Evaluation of Selected Biometric Fusion Techniques”, Studies of Biometric Fusion report, National Institute of Standards and Technology,pp.1-14, 2006.
  3. C. Watson, C. Wilson, M. Indovina, B. Cochran; “Two Finger Matching With Vendor SDK Matchers”; NIST Interagency Report 7249; July 2005.
  4. Jonas Richiardi and Andrzej Drygajlo, “Gaussian Mixture Models for Online Signature Verification”, Speech Processing Group Signal Processing Institute Swiss Federal Institute of Technology (EPFL), WBMA’03, Berkeley, California, USA.ACM 1,pp.771-779,2003.
  5. Ross A and Jain A., “Multimodal biometrics: An overview”. Proc. of the 12th European Signal Processing Conference, pages 1221–1224. 2004.
  6. L. Rabiner and B.-H. Juang, Fundamentals of Speech Recognition. Englewood Cliffs, NJ: Prentice-Hall, 1993.
  7. Richiardi J. and Drygajlo A., “Gaussian mixture models for on-line signature verification,” in Proc. ACM SIGMM Workshop Biometrics Methods Appl., pp. 115–122, 2003.
  8. Liwicki M., Schlapbach A., Bunke H., Bengio S., Mariéthoz J., and Richiardi J., “Writer identification for smart meeting room systems,” in Proc. 7th International Workshop Document Anal. System New York: Springer-Verlag, Vol. 3872, pp. 186–195, 2006.
  9. Reynolds D., “An overview of automatic speaker recognition technology”, Proc. IEEE International Conference. Acoustic, Speech, Signal Process, Vol. 4, pp. 4072–4075, 2002.
  10. Bimbot F.et al., “A tutorial on text-independent speaker verification”, EURASIP J. Application Signal Process., Vol.4, pp. 430–451, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Biometrics Multimodal intra-personal variability False Accept Rate (FAR) False Reject Rate (FRR)