Encyclopedia of Biometrics

2009 Edition
| Editors: Stan Z. Li, Anil Jain

Fusion, Sensor-Level

  • Afzel Noore
  • Richa Singh
  • Mayank Vasta
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-73003-5_156



Sensor level fusion combines raw biometric information that can account for inter-class and intra-class variability and facilitate decision making based on the fused raw information. A typical sensor level fusion algorithm first integrates raw biometric data either obtained from different viewpoints (for example, mosaicing several fingerprint impressions) or obtained from different sensors (for example, multimodal biometric images). The integrated data is then processed and discriminatory biometric features are extracted for matching. This level of fusion can be operated in both verification and  identification modes. Few examples of sensor level fusion are: fingerprint mosaicing, multi-spectral face image fusion, and multimodal biometric image fusion.


The concept of biometric information fusion is motivated from classical multi-classifier systems that combine information from different sources and represent...
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  1. 1.
    Ross, A., Nandakumar, K., Jain, A.K.: Handbook of Multibiometrics. 1st edn. Springer, New York (2006)Google Scholar
  2. 2.
    Singh, R., Vatsa, M., Ross, A., Noore, A.: A mosaicing scheme for pose-invariant face recognition. IEEE Trans. Syst. Man Cybern. Part B 37(5), 1212–1225 (2007)CrossRefGoogle Scholar
  3. 3.
    Ratha, N.K., Connell, J.H., Bolle, R.M.: Image mosaicing for rolled fingerprint construction. In: Proceedings of International Conference on Pattern Recognition, pp. 1651–1653 (1998)Google Scholar
  4. 4.
    Jain, A., Ross, A.: Fingerprint mosaicking. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, pp. 4064–4067 (2002)Google Scholar
  5. 5.
    Ross, A., Shah, S., Shah, J.: Image versus feature mosaicing: a case study in fingerprints. In: Proceedings of SPIE Conference on Biometric Technology for Human Identification III, pp. 620,208-1–620,208-12 (2006)Google Scholar
  6. 6.
    Yang, F., Paindavoine, M., Abdi, H., Monopoly, A.: Development of a fast panoramic face mosaicing and recognition system. Opt. Eng. 44(8), 087 005/1–087 005/10 (2005)Google Scholar
  7. 7.
    Lu, X., Jain, A.K.: Pose-robust face recognition using geometry assisted probabilistic modeling. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 502–509 (2005)Google Scholar
  8. 8.
    Kong, S., Heo, J., Abidi, B., Paik, J., M., A.: Recent advances in visual and infrared face recognition - a review. Comput. Vision Image Understand. 97(1), 103–135 (2005)CrossRefGoogle Scholar
  9. 9.
    Bebis, G., Gyaourova, A., Singh, S., Pavlidis, I.: Face recognition by fusing thermal infrared and visible imagery. Image Vision Comput. 24(7), 727–742 (2006)CrossRefGoogle Scholar
  10. 10.
    Kong, S., Heo, J., Boughorbel, F., Zheng, Y., Abidi, B., Koschan, A., Yi, M., M., A.: Multiscale fusion of visible and thermal IR images for illumination-invariant face recognition. Int. J. Comput. Vision 71(2), 215–233 (2007)CrossRefGoogle Scholar
  11. 11.
    Singh, R., Vatsa, M., Noore, A.: Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition. Pattern Recognit. 41(3), 880–893 (2008)MATHCrossRefGoogle Scholar
  12. 12.
    Lu, X., Jain, A.K.: Integrating range and texture information for 3D face recognition. In: Proceedings of Workshop on Applications of Computer Vision, pp. 156–163 (2005)Google Scholar
  13. 13.
    Jing, X.Y., Yao, Y.F., Zhang, D., Yang, J.Y., Li, M.: Face and palmprint pixel level fusion and Kernel DCV-RBF classifier for small sample biometric recognition. Pattern Recognit. 40(11), 3209–3224 (2007)MATHCrossRefGoogle Scholar
  14. 14.
    Noore, A., Singh, R., Vatsa, M.: Robust memory efficient data level information fusion of multi-modal biometric images. Inf. Fusion 8(4), 337–346 (2007)CrossRefGoogle Scholar
  15. 15.
    Vatsa, M., Singh, R., Noore, A.: Unification of evidence theoretic fusion algorithms: A case study in level-2 and level-3 fingerprint features. In: Proceedings of IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6 (2007)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Afzel Noore
    • 1
  • Richa Singh
    • 1
  • Mayank Vasta
    • 1
  1. 1.West Virginia UniversityMorgantownUSA