Multimodal Biometric Invariant Fusion Techniques

  • P. Viswanatham
  • P. Venkata KrishnaEmail author
  • V. Saritha
  • Mohammad S. Obaidat


The hand geometry, features in face, iris scan, and fingerprint vary from person to person, which provide unique features to be used in biometrics field for providing security to various systems. Most of the mono-biometric authentication systems give high error rate as they use only one feature. Hence, multimodal biometric systems are introduced, which can help in reducing the error rate at the cost of maintaining more data related to the features. Hence, it is said to be that the multimodal biometric systems are more reliable and secure. Image-based approaches offer much higher computation efficiency with minimum preprocessing. This approach is proved to be effective as the reliable feature extraction is possible even when the quality of image is low. However, this approach is weak if there are distortions in the shape of the image and variation in the positions or the orientation angle. Hence, this chapter presents a multimodal biometric invariant fusion authentication system based on fusion of Zφ invariant moment of fingerprint and face features. It reduces the storage of more features for authentication and reduces the error rate. The Morlet wavelet transform is used to make the system less sensitive to shape distortion by smoothening and preserving the local edges. The Zφ moment is the combination of Zernike and invariant moments, which are used to produce an affine transformation that is extracted from the fingerprint and the face. Authentication is successful if the similarity is 90% in the case of fingerprint and 70% in the case of face. False acceptance rate (FAR) and false reject rate (FRR) are optimal with these threshold values.


Biometrics Multimodal Authentication Fingerprint Face Iris scan FAR FRR 


  1. 1.
    M.S. Obaidat, N. Boudriga, Security of e-Systems and Computer Networks (Cambridge University Press, Cambridge, UK, 2007)CrossRefGoogle Scholar
  2. 2.
    M.S. Obaidat, B. Sadoun, Verification of computer users using keystroke dynamics. IEEE Trans. Syst. Man Cybern. B 27(2), 261–269 (1997)CrossRefGoogle Scholar
  3. 3.
    M.S. Obaidat, B. Sadoun, Keystroke dynamics based identification, in Biometrics: Personal Identification in Networked Society, ed. by A. Jain et al. (Springer, Kluwer, 1999), pp. 213–229Google Scholar
  4. 4.
    W. Stallings, Cryptography and Network Security- Principles and Practices (Prentice-Hall, Upper Saddle River, 2003)Google Scholar
  5. 5.
    T. Jea, V. Govindaraju, A minutia-based partial fingerprint recognition system. Pattern Recogn. 38(10), 1672–1684 (2005)CrossRefGoogle Scholar
  6. 6.
    T. Jea, V.K. Chavan, V. Govindaraju, J.K. Schneider, Security and matching of partial fingerprint recognition systems. Proc. SPIE 5404, 39–50 (2004)CrossRefGoogle Scholar
  7. 7.
    D. Maio, D. Maltoni, A.K. Jain, S. Prabhakar, Handbook of Fingerprint Recognition (Springer, Berlin, 2003)zbMATHGoogle Scholar
  8. 8.
    P. Viswanathan, P. Venkata Krishna, Fingerprint enhancement and compression method using Morletwavelet. Int. J. Signal Imaging Syst. Eng. 3(4), 261–268 (2010)CrossRefGoogle Scholar
  9. 9.
    S. Prabhakar, J. Wang, A. K. Jain, S. Pankanti, R. Bolle. Minutiae verification and classification for fingerprint matching. In Proc. 15th International Conference Pattern Recognition, Vol. 1, Barcelona, September 3–8, 2000, pp. 25–29Google Scholar
  10. 10.
    J. Liu, Z. Huang, K. Chan, Direct minutiae extraction from gray-level fingerprint image by relationship examination. Proc. Int. Conf. Image Process. 2, 427–430 (2000)Google Scholar
  11. 11.
    P. Viswanathan, P. Venkata Krishna, Morlet Wavelet fingerprint invariant automated authentication system. Int. J. Recent Trends Eng. 4(1), 1–5 (2010)Google Scholar
  12. 12.
    C. Chen, Decision level fusion of hybrid local features for face recognition. In Neural networks and signal Processing, 2008 International Conference on (pp. 199–204). IEEE (2008).Google Scholar
  13. 13.
    L.F. Sha, F. Zhao, X.O. Tang, Improved finger code for filter bank-based fingerprint matching. Proc. Int. Conf. Image Process. 2, 895–898 (2003)Google Scholar
  14. 14.
    M. Tico, E. Immonen, P. Ramo, P. Kuosmanen, J. Saarinen, Fingerprint recognition using wavelet features. Proc. IEEE Int. Symp. Circuits Syst. 2, 21–24 (2001)Google Scholar
  15. 15.
    T. Amornraksa, S. Achaphetpiboon, Fingerprint recognition using DCT features. Electron. Lett. 42(9), 522–523 (2006)CrossRefGoogle Scholar
  16. 16.
    A.T.B. Jin, D.N.C. Ling, O.T. Song, An efficient fingerprint verification system using integrated wavelet and Fourier-Mellin invariant transform. Image Vis. Comput. 22(6), 503–513 (2004)CrossRefGoogle Scholar
  17. 17.
    D. Maio, D. Maltoni, Direct gray scale minutia detection in fingerprints. Trans. PAMI 19(1), 27–40 (1997)CrossRefGoogle Scholar
  18. 18.
    P. Viswanathan, P. VenkataKrishna, Multimodal biometric invariant moment fusion authentication system. Information Management Processing, BAIP 2010, Springer CCIS, vol 70, 2010, pp. 136–144Google Scholar
  19. 19.
    G.L. Marcialis, F. Roli, Score-level fusion of fingerprint and face matchers for personal verification under “stress” conditions. In 14th International Conference on Image Analysis and Processing (ICIAP 2007) 0-7695-2877-5/07 $25.00 © 2007 IEEEGoogle Scholar
  20. 20.
    A. Rattani, D.R. Kisku, M. Bicego, M. Tistarelli, Feature level fusion of face and fingerprint biometrics 978-1-4244-1597-7/07/$25.00 ©2007 IEEEGoogle Scholar
  21. 21.
    T.-Y. Jea, V. Govindaraju, A minutia-based partial fingerprint recognition system. Pattern Recogn. 38(10), 1672–1684 (2005)CrossRefGoogle Scholar
  22. 22.
    C.I. Watson, G.T. Candela, P.J. Grother, Comparison of FFT fingerprint filtering methods for neural network classification. NISTIR 5493 (1994) Available: Scholar
  23. 23.
    M.K. Hu, Visual pattern recognition by moment invariants. IRE Trans. Info. Theory IT-8, 179–187 (1962)zbMATHGoogle Scholar
  24. 24.
    J.C. Yang, D.S. Park, Fingerprint verification based on invariant moment features and nonlinear BPNN. Int. J. Control. Autom. Syst. 6(6), 800–808 (2008)Google Scholar
  25. 25.
    L. O’Gormann, J.V. Nickerson, An approach to fingerprint filter design. Pattern Recogn. 22(1), 29–38 (1989)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • P. Viswanatham
    • 1
  • P. Venkata Krishna
    • 2
    Email author
  • V. Saritha
    • 3
  • Mohammad S. Obaidat
    • 4
  1. 1.School of Information Technology and Engineering, VIT UniversityVelloreIndia
  2. 2.Department of Computer ScienceSri Padmavati Mahila VisvavidyalayamTirupatiIndia
  3. 3.Department of Computer Science and EngineeringSri Padmavati Mahila VisvavidyalayamTirupatiIndia
  4. 4.Department of Computer and Information ScienceFordham UniversityNew YorkUSA

Personalised recommendations