Analyzing State-of-the-Art Techniques for Fusion of Multimodal Biometrics

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 381)


Multimodal systems used for face recognition can be broadly classified into three categories: score level fusion, decision level fusion, and feature level fusion. In this paper, we have analyzed the performance of the three categories on various standard public databases such as Biosecure DS2, FERET, VidTIMIT, AT&T, USTB I, USTB II, RUsign, and KVKR. From our analysis, we found that score level fusion approach can effectively fuse multiple biometric modalities, and is robust to operate in less constrained conditions. In the decision fusion scheme, each decision is made after the improvement of the classifier confidence hence the recognition rate obtained is less compared to score level fusion. Feature level fusion requires less information and performs better than decision level fusion, but its recognition rate is less compared to score level fusion.


Multimodal biometrics Score level fusion Decision level fusion Feature level fusion 



The proposed work was made possible because of the grant provided by Vision Group Science and Technology (VGST), Department of Information Technology, Biotechnology and Science and Technology, Government of Karnataka, Grant No. VGST/SMYSR/GRD-402/2014-15 and the support provided by Department of Electronics and Communication Engineering, Karunya University, Coimbatore, Tamil Nadu, India.


  1. 1.
    Prabhakar, S., Pankanti, S., et al.: Biometric recognition security and privacy concerns. IEEE Secur. Priv. 1(2), 33–42 (2003)CrossRefGoogle Scholar
  2. 2.
    Jain, A.K., Flynn, P., et al.: Handbook of Biometrics. Springer, New York (2007)Google Scholar
  3. 3.
    Down, M.P., Sands, R.J.: Biometrics: an overview of the technology, challenges and control considerations. Inf. Syst. Control J. 4, 53–56 (2004)Google Scholar
  4. 4.
    Ross, A., Govindarajan, R.: Feature level fusion using hand and face biometrics. In: Proceedings of the SPIE 2nd Conference Biometric Technology Human Identification, Orlando, USA, pp. 196–204 (2004)Google Scholar
  5. 5.
    Chang, K., Bower, K.W., et al.: Comparison and combination of ear and face images in appearance-based biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1160–1165 (2003)CrossRefGoogle Scholar
  6. 6.
    Marcialis, G.L., Roli, F.: Fingerprint verification by fusion of optical and capacitive sensors. Pattern Recognit. Lett. 25(11), 1315–1322 (2004)CrossRefGoogle Scholar
  7. 7.
    Ross, A., Jain, A.K.: Information fusion in biometrics. Patter Recognit. Lett. 24(13), 2115–2125 (2003)CrossRefGoogle Scholar
  8. 8.
    Kinnunen, T., Hautamäki, V., et al.: Fusion of spectral feature sets or accurate speaker identification. In: Proceedings of the 9th Conference Speech Computer, pp. 361–365 (2004)Google Scholar
  9. 9.
    Ross, A., Jain, A.: Information fusion in biometrics. Pattern Recogn. Lett. 24, 2115–2125 (2003)Google Scholar
  10. 10.
    Yang, F., Ma, B.: A new mixed-mode biometrics information fusion based-on fingerprint: hand-geometry and palm-print. In: Proceedings of the IEEE Conference Image Graph, pp. 689–693 (2007)Google Scholar
  11. 11.
    Cui, J., Li, J.P., et al.: Study on multibiometric feature fusion and recognition model. In: Proceedings of the IEEE Conference Apperceiving Computing and Intelligence Analysis pp. 66–69 (2008)Google Scholar
  12. 12.
    Dahel, S.K., Xiao, Q.: Accuracy performance analysis of multimodal biometrics. Information Assurance Workshop, IEEE Systems Man and Cybernetics Society, pp. 170–173 (2003)Google Scholar
  13. 13.
    Ross, A., Nandakumar, K., et al.: Handbook of Multibiometrics. Springer, Germany (2006)Google Scholar
  14. 14.
    Ross, A., Nandakumar, K., et al.: Handbook Of Multibiometrics. Springer, USA (2011)Google Scholar
  15. 15.
    Nguyen, K., Denman, S., et al.: Score-level multibiometric fusion based on dempster–shafer theory incorporating uncertainty factor. IEEE Trans. Hum. Mach. Syst. 45(1) (2015)Google Scholar
  16. 16.
    Paul, P.P., Gavrilova, M.L., et al.: Decision fusion for multimodal biometrics using social network analysis. IEEE Trans. Syst. Man Cybern. Syst. 44(11) (2014)Google Scholar
  17. 17.
    Kale, K.V., Rode, Y.S., et al.: Multimodal biometric system using fingernail and finger knuckle (2013)Google Scholar
  18. 18.
  19. 19.
    The Facial Recogniton Technology Database:
  20. 20.
  21. 21.
  22. 22.
  23. 23.
    Fernandes, S.L., Josemin Bala, G.: 3D and 4D face recognition: a comprehensive review. Recent Pat. Eng. 8(2), 112–119 (2014)Google Scholar
  24. 24.
    Fernandes, S.L., Josemin Bala, G.: Development and analysis of various state of the art techniques for face recognition under varying poses. Recent Pat. Eng. 8(2), 143–146 (2014)Google Scholar
  25. 25.
    Fernandes, S.L., Josemin Bala, G.: Recognizing faces when images are corrupted by varying degree of noises and blurring effects. Adv. Intell. Syst. Comput. 337(1), 101–108 (2015)CrossRefGoogle Scholar
  26. 26.
    Fernandes, S.L., Josemin Bala, G.: Low power affordable, efficient face detection in the presence of various noises and blurring effects on a single-board computer. Adv. Intell. Syst. Comput. 337(1), 119–127 (2015)CrossRefGoogle Scholar
  27. 27.
    Fernandes, S.L., Josemin Bala, G.: Recognizing facial images in the presence of various noises and blurring effects using gabor wavelets, DCT neural network, hybrid spatial feature interdependence matrix. In: 2nd IEEE International Conference on Devices, Circuits and Systems (2014)Google Scholar
  28. 28.
    Fernandes, S.L., Josemin Bala, G.: Recognizing facial images using ICA, LPP, MACE gabor filters, score level fusion techniques. In: IEEE International Conference Electronics and Communication Systems (2014)Google Scholar
  29. 29.
    Fernandes, S.L., Josemin Bala, G., et al.: Robust face recognition in the presence of noises and blurring effects by fusing appearance based techniques and sparse representation. In: IEEE International Conference on Advanced Computing, Networking and Security (2013)Google Scholar
  30. 30.
    Fernandes, S.L., Josemin Bala, G., et al.: A comparative study on score level fusion techniques and MACE gabor filters for face recognition in the presence of noises and blurring effects. In: IEEE IEEE International Conference on Cloud and Ubiquitous Computing and Emerging Technologies (2013)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department Electronics and Communication EngineeringKarunya UniversityCoimbatoreIndia

Personalised recommendations