List of Journals    /    Call For Papers    /    Subscriptions    /    Login
 
 
 
 
 SEARCH
By Author By Title
 
 
ABOUT CSC
 About CSC Journals
 CSC Journals Objectives
 List of Journals
 CALL FOR PAPERS
 Call For Papers CFP
 Special Issue CFP
AUTHOR GUIDELINES
 Submission Guidelines
 Peer Review Process
 Helpful Hints For Getting Published
 Plagiarism Policies
 Abstracting & Indexing
 Open Access Policy
 Submit Manuscript
 FOR REVIEWERS
 Reviewer Guidelines
 FOR EDITORIAL
 Editor Guidelines
 Join Us As Editor
 Launch Special Issue
 Suggest New Journal
 CSC LIBRARY
 Browse CSC Library
 Open Access Policy
  SERVICES
 Conference Partnership Program (CPP)
 Abstracting & Indexing
 SUBSCRIPTIONS
 Subscriptions
 Discounted Packages
 Archival Subscriptions
 How to Subscribe
 Librarians
 Subscriptions Agents
 Order Form
 DOWNLOADS
 
 
 
 
Bimodal Biometric Person Authentication System Using Speech and Signature Features
Full text
 PDF(163.4KB)
Source 
International Journal of Biometrics and Bioinformatics (IJBB)
Table of Contents
Download Complete Issue    PDF(2.44MB)
Volume:  4    Issue:  4
Pages:  136-160
Publication Date:   September 2010
ISSN (Online): 1985-2347
Pages 
147 - 160
Author(s)  
 
Published Date   
30-10-2010 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Speaker recognition, Signature verification, Multimodal biometrics, Biometrics 
 
 
This Manuscript is indexed in the following databases/websites:-
1. Docstoc
2. Scribd
3. Directory of Open Access Journals (DOAJ)
4. PDFCAST
5. refSeek
6. Academic Index
7. iSEEK
8. Socol@r
9. Google Scholar
10. WorldCat
11. Bielefeld Academic Search Engine (BASE)
12. ResearchGATE
13. Academic Journals Database
14. Libsearch
 
 
Biometrics offers greater security and convenience than traditional methods of person authentication. Multi biometrics has recently emerged as a means of more robust and efficient person authentication scheme. Exploiting information from multiple biometric features improves the performance and also robustness of person authentication. The objective of this paper is to develop a robust bimodal biometric person authentication system using speech and signature biometric features. Speaker based unimodal system is developed by extracting Mel Frequency Cepstral Coefficients (MFCC) and Wavelet Octave Coefficients of Residues (WOCOR) as feature vectors. The MFCCs and WOCORs from the training data are modeled using Vector Quantization (VQ) and Gaussian Mixture Modeling (GMM) techniques. Signature based unimodal system is developed by using Vertical Projection Profile (VPP), Horizontal Projection Profile (HPP) and Discrete Cosine Transform (DCT) as features. A bimodal biometric person authentication system is then built using these two unimodal systems. Experimental results show that the bimodal person authentication system provides higher performance compared with the unimodal systems. The bimodal system is finally evaluated for its robustness using the noisy data and also data collected from the real environments. The robustness of the bimodal system is more compared to the unimodal person authentication systems.  
 
 
 
1 A. Jain L.Hang and S. Pankanti. “Can multi-biometrics improve performance,” Proceedings of Auto ID, 59-64, 1999.
2 L. Gorman, “Comparing passwords, tokens, and biometrics for user authentication,” IEEE Proceedings, vol 91, no12, Dec 2003.
3 A.K. Jain, A. Ross and Prabhaker, “An introduction to Biometric Recognition,” IEEE Transaction on Circuits and Systems for Video Technology, vol 14, no.1, 4-20, Jan 2004.
4 R. Bruneeli and D.Falavigna, “Person identification using multiple cues,” IEEE Transaction, PAMI, vol 12, no10, 955-966, Oct.1995.
5 A. K. Jain and L. Hong, “Integrating faces and fingerprints for person identification,” IEEE Transaction, Pattern Analysis and Machine Intelligence (PAMI), vol.20, no12, 1295-1307, Dec 1998.
6 V. Ghatis, A.G. Bors and I.Pitas, “Multimodal decision level fusion for person authentication,” IEEE Transaction and Systems, Man and Cybernetics, vol 29, no6, 674-680, Nov. 1999.
7 R.W. Frischolz and U Dieckman, “Biod: a multimodal biometric identification system,” IEEE Computer, vol.33, 64-68, Feb 2000.
8 B. Duc et. Al., “Fusion of audio and video information for multimodal person authentication,” Pattern Recognition letters, vol.18, 835-843, 1997.
9 B.S. Atal, “Automatic recognition of speakers from their voices,” IEEE Proceedings, vol. 64, no. 4, 460-75, Apr 1976.
10 A.E. Rosenberg, “Automatic Speaker verification: A review,” IEEE Proceedings, vol 64, no.4, 475-487, Apr. 1976.
11 H. Gish, and M. Schmidt, “Text-independent speaker identification,” IEEE Signal Process, Magazine, vol 18, 18-32, Oct. 2002.
12 A. Eriksson and P.Wretling, “How flexible is the Human Voice? A case study of Mimicry,” Proceedings of European Conference on Speech Technology, Rhodes,1043-1046, 1997.
13 D.A. Reynolds, “Speaker identification and verification using Gaussian mixture speaker models,” Speech Communication., vol 17, no1-2, 91-108, 1995.
14 S.R.M. Prasanna, C.S. Gupta, and B. Yegnanarayana, “Extraction of speaker specific excitation information from linear prediction residual of speech,” Speech Communication., vol 48, 1243-1261, Oct. 2006.
15 L. hanzo, F.C.A. Somerville and J.P. Woodard,” Voice compression and Communications,” John B. Anderson, Wiley IEEE Pres series, 2001.
16 Y. Linde, A.Buzo and R.M. Gray, “An algorithm for Vector Quantizer Design,” IEEE Transaction on Communications, vol, COM_28, no.1, 84-96,Jan 1980.
17 R. Gray, “Vector quantization,” IEEE Acoustic Speech Signal Process, Magazine, vol 1, 4-29, Apr.1984.
18 V.S. Nalwa, “Automatic on-line Signature verification,” IEEE Proceedings, vol 85, no.2, 213- 239, Feb.1997.
19 W. Hou, X. Ye and K. Wang, “A survey of offline signature verification,” IEEE Proceedings, International Conference on Intelligent Mechatronics and Automation, 536-541, Aug 2004.
20 Chaur-Heh Hsieh, “DCT based code book design for vector quantization of images,” IEEE Transactions, Circuits and Systems for Video Technology, vol.2, no.4, 401-409, Dec1992.
21 Makhoul J., “Linear Prediction: a Tutorial review”, IEEE Proceedings, 561-580, Oct 1975.
22 L. Rabiner and B.H. Jung, “Fundamentals of Speech Recognition”, Pearson Education, 326- 396(1993).
23 T.M. Math and R. Manmatha, “Word image matching using dynamic time warping”, IEEE Proceedings, computer Vision and Pattern Recognition, vol.2, 521-527, June 2003.
24 A. Jain, K. Nanda Kumar and A.Ross, “Score normalization in multimodal biometric systems”, Pattern Recognition Journal- Elsevier, vol.38, 2270-2285, Jan. 2005.
25 F.Alsaade, “Score-Level fusion for multibiometrics”, PhD Thesis, University of Hertfordshire, Jan.2008.
26 B.S.Atal “Effectiveness of Linear prediction characteristics of the speech wave for Automatic Speaker Identification and Verification”, J. Acoust, Soc. Amer., 55(6); 1304-1312,1974.
27 S.Furu, “Cepstral Analysis Technique for Automatic Speaker Verification”, IEEE Transaction, Acoustic and Speech Signal Processing, ASSp-29(2): 254-272, 1981.
28 G.Strang and T.Nguyen, “Wavelets and Filter Banks”, Wellesley-Cambridge Press, 1996.
29 A. Sanker and C.H.Lee, “A Maximum-Likelihood approach to stochastic matching for robust speech recognition”, IEEE Transaction, Speech-Audio Processing, 4(3): 190-202, 1996.
30 L.R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”, IEEE Proceedings, 77/27, 257-286,1989.
31 L.E. Baum and T. Petie, “Statistical inference for probabilistic functions of finite state Markov chains”, Ann. Mat. Stat., 37; 1554-1563, 1966.
32 D.Talkin, “A Robust Algorithm for Pitch Tracking (RAPT)”, Speech Coding and Synthesis, W.B. Kleja and K.K. Paliwal, Eds., New York, Elsevier 1995.
33 I. Daubechies, “Ten Lectures on Wavelets”, Philadelphia, PA: Siam, vol.6, 36-106,1992.
34 ZHENG Nengheng, “Speaker Recognition using Complementary Information from Vocal Source and Vocal Tract”, PhD Thesis, The Chinese University of Hong Kong, Nov. 2005.
35 A.Ross and A.k.Jain, “Multimodal Biometrics: an Overview”, Proceedings of 12th European Signal Conference (EUSIPCO), 1221-1224, Sept.2004.
 
 
 
1 A. Alford, K. Popplewell,G. Dozier, K. Bryant, J. Kelly, J. Adams, T. Abegaz and J. Shelton, “GEFeWS: A Hybrid Genetic-Based Feature Weighting and Selection Algorithm for Multi-Biometric Recognition”, in Proceedings of The 22nd Midwest Artificial intelligence and Cognitive Science Conference (MAICS), 2011.
2 S.M.E. Hossain, G. Chetty ,(2011), “Next Generation Identity Verification Based on Face-Gait Biometrics” in Proceedings of International Conference on Biomedical Engineering and Technology(ICBET 2011) Kuala Lumpur, Malaysia. June 17-19, 2011.
3 E. Hossain and G. Chetty , “Person Identity Verification Based on Multimodal Face-Gait Fusion”, International Journal of Computer Science and Network Security, 11(6), pp. 77-86, June 2011.
4 S. M. E. Hossain abd G. Chetty,, “Human Identity Verification by Using Physiological and Behavioural Biometric Traits”, International Journal of Bioscience, Biochemistry and Bioinformatics, 1(3), pp. 199-205, September 2011.
 
 
 
1 TechRepublic
 
2 ZDNet
 
3 silicon.com
 
4 BioPortfolio
 
 
 
Eshwarappa M.N. : Colleagues
Dr. Mrityunjaya V.Latte : Colleagues  
 
 
 
  Untitled Document
 
Copyrights (c) 2012 Computer Science Journals. All rights reserved.
Best viewed at 1152 x 864 resolution. Microsoft Internet Explorer.
 
  
 
Copyrights & Usage: Articles published by CSC Journals are Open Access. Permission to copy and distribute any other content, images, animation and other parts of this website is prohibited. CSC Journals has the rights to take action against individual/group if they are found victim of copying these parts of the website.