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Joint Sparsity-Based Robust Multimodal Biometrics Recognition

  • Sumit Shekhar
  • Vishal M. Patel
  • Nasser M. Nasrabadi
  • Rama Chellappa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)

Abstract

Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a novel multimodal multivariate sparse representation method for multimodal biometrics recognition, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information between biometric modalities. Furthermore, the model is modified to make it robust to noise and occlusion. The resulting optimization problem is solved using an efficient alternative direction method. Experiments on a challenging public dataset show that our method compares favorably with competing fusion-based methods.

Keywords

Sparse Representation Augmented Lagrangian Method Alternative Direction Method Biometric Trait Feature Level Fusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ross, A., Jain, A.K.: Multimodal biometrics: an overview. In: Proc. European Signal Processing Conference, pp. 1221–1224 (2004)Google Scholar
  2. 2.
    Klausner, A., Tengg, A., Rinner, B.: Vehicle classification on multi-sensor smart cameras using feature- and decision-fusion. In: IEEE Conf. Dist. Smart Cameras, pp. 67–74 (2007)Google Scholar
  3. 3.
    Rattani, A., Kisku, D., Bicego, M., Tistarelli, M.: Feature level fusion of face and fingerprint biometrics. In: IEEE Int. Conf. on Biometrics: Theory, Applications, and Systems, pp. 1–6 (2007)Google Scholar
  4. 4.
    Ross, A.A., Govindarajan, R.: Feature level fusion of hand and face biometrics. In: Proc. of the SPIE, vol. 5779, pp. 196–204 (2005)Google Scholar
  5. 5.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 210–227 (2009)CrossRefGoogle Scholar
  6. 6.
    Patel, V.M., Chellappa, R.: Sparse representations, compressive sensing and dictionaries for pattern recognition. In: Asian Conference on Pattern Recognition (2010)Google Scholar
  7. 7.
    Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B 68, 49–67 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Meier, L., Geer, S.V.D., Bhlmann, P.: The group lasso for logistic regression. Journal of the Royal Statistical Society: Series B 70, 53–71 (2008)zbMATHCrossRefGoogle Scholar
  9. 9.
    Nguyen, N.H., Nasrabadi, N.M., Tran, T.D.: Robust multi-sensor classification via joint sparse representation. In: International Conference on Information Fusion (2011)Google Scholar
  10. 10.
    Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B 58, 267–288 (1996)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Yang, J., Zhang, Y.: Alternating direction algorithms for l1 problems in compressive sensing. SIAM Journal on Scientific Computing 33, 250–278 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Afonso, M., Bioucas-Dias, J., Figueiredo, M.: An augmented lagrangian approach to the constrained optimization formulation of imaging inverse problems. IEEE Transactions on Image Processing 20, 681–695 (2011)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Crihalmeanu, S., Ross, A., Schuckers, S., Hornak, L.: A protocol for multibiometric data acquisition, storage and dissemination. Technical Report, WVU, Lane Department of Computer Science and Electrical Engineering (2007)Google Scholar
  14. 14.
    Pundlik, S., Woodard, D., Birchfield, S.: Non-ideal iris segmentation using graph cuts. In: IEEE CVPR Workshop, pp. 1–6 (2008)Google Scholar
  15. 15.
    Masek, L.: Recognition of human iris patterns for biometric identification. Technical report, The University of Western Australia (2003)Google Scholar
  16. 16.
    Chikkerur, S., Wu, C., Govindaraju, V.: A systematic approach for feature extraction in fingerprint images. In: Int. Conference on Bioinformatics and its Applications, p. 344 (2004)Google Scholar
  17. 17.
    Jain, A., Prabhakar, S., Hong, L., Pankanti, S.: Filterbank-based fingerprint matching. IEEE Transactions on Image Processing 9, 846–859 (2000)CrossRefGoogle Scholar
  18. 18.
    Krishnapuram, B., Carin, L., Figueiredo, M., Hartemink, A.: Sparse multinomial logistic regression: fast algorithms and generalization bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 957–968 (2005)CrossRefGoogle Scholar
  19. 19.
    Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sumit Shekhar
    • 1
  • Vishal M. Patel
    • 1
  • Nasser M. Nasrabadi
    • 2
  • Rama Chellappa
    • 1
  1. 1.University of MarylandCollege ParkUSA
  2. 2.Army Research LabAdelphiUSA

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