Variability Compensation Using NAP for Unconstrained Face Recognition

  • Pedro TomeEmail author
  • Ruben Vera-Rodriguez
  • Julian Fierrez
  • Javier Ortega-García
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 151)


The variability presented in unconstrained environments represents one of the open challenges in automated face recognition systems. Several techniques have been proposed in the literature to cope with this problem, most of them tailored to compensate one specific source of variability, e.g., illumination or pose. In this paper we present a general variability compensation scheme based on the Nuisance Attribute Projection (NAP) that can be applied to compensate for any kind of variability factors that affects the face recognition performance. Our technique reduces the intra-class variability by finding a low dimensional variability subspace. This approach is assessed on a database from the NIST still face recognition challenge “The Good, the Bad, and the Ugly” (GBU). The results achieved using our implementation of a state-of-the-art system based on sparse representation are improved significantly by incorporating our variability compensation technique. These results are also compared to the GBU challenge results, highlighting the benefits of adequate variability compensation schemes in these kind of uncontrolled environments.


Face Recognition Face Image Sparse Representation Speaker Recognition Baseline System 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Candes, E., Romberg, J.: l1-magic: Recovery of sparse signals via convex programming (2005),
  2. 2.
    Huang, K., Aviyente, S.: Sparse representation for signal classification, pp. 609–616 (2006)Google Scholar
  3. 3.
    ISO/IEC JTC 1/SC 37 N 504. Biometric data interchange formats part 5: Face image (2004)Google Scholar
  4. 4.
    Li, S.Z., Chu, R., Liao, S., Zhang, L.: Illumination invariant face recognition using near-infrared images. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 627–639 (2007)CrossRefGoogle Scholar
  5. 5.
    Li, S.Z., Schouten, B., Tistarelli, M.: Biometrics at a Distance: Issues, Challenges, and Prospects. In: Handbook of Remote Biometrics for Surveillance and Security, pp. 3–21. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Phillips, P.J., Beveridge, J.R., Draper, B.A., Givens, G., O’Toole, A.J., Bolme, D.S., Dunlop, J., Lui, Y.M., Sahibzada, H., Weimer, S.: An introduction to the good, the bad, amp; the ugly face recognition challenge problem. In: Int. Conf. on Automatic Face Gesture Recognition and Workshops (FG 2011), pp. 346–353 (March 2011)Google Scholar
  7. 7.
    Phillips, P.J., Flynn, P.J., Beveridge, J.R., Scruggs, W.T., O’Toole, A.J., Bolme, D., Bowyer, K.W., Draper, B.A., Givens, G.H., Lui, Y.M., Sahibzada, H., Scallan III, J.A., Weimer, S.: Overview of the Multiple Biometrics Grand Challenge. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 705–714. Springer, Heidelberg (2009), CrossRefGoogle Scholar
  8. 8.
    Phillips, P.J., Scruggs, W.T., O’Toole, A.J., Flynn, P.J., Bowyer, K.W., Schott, C.L., Sharpe, M.: FRVT 2006 and ICE 2006 large-scale experimental results. IEEE Transactions on Pattern Analysis and Machine Intelligence 99 (2009)Google Scholar
  9. 9.
    Solomonoff, A., Campbell, W.M., Quillen, C.: Nuisance attribute projection. In: Speech Communication. Elsevier Science BV, Amsterdam (2007)Google Scholar
  10. 10.
    Solomonoff, A., Quillen, C., Campbell, W.M.: Channel compensation for svm speaker recognition. In: Proceedings on Odyssey: The Speaker and Language Recognition Workshop, Toledo, Spain, pp. 41–44 (2004)Google Scholar
  11. 11.
    Tome, P., Fierrez, J., Alonso-Fernandez, F., Ortega-Garcia, J.: Scenario-based score fusion for face recognition at a distance. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 67–73 (June 2010)Google Scholar
  12. 12.
    Tome, P., Fierrez, J., Fairhurst, M.C., Ortega-Garcia, J.: Acquisition Scenario Analysis for Face Recognition at a Distance. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammoud, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6453, pp. 461–468. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Štruc, V., Vesnicer, B., Mihelič, F., Pavešić, N.: Removing illumination artifacts from face images using the nuisance attribute projection. In: Proceedings of the IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2010), Dallas, Texas, USA, pp. 846–849 (March 2010)Google Scholar
  14. 14.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)CrossRefGoogle Scholar
  15. 15.
    Zhang, X., Gao, Y.: Face recognition across pose: A review. Pattern Recognition, 2876–2896 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pedro Tome
    • 1
    Email author
  • Ruben Vera-Rodriguez
    • 1
  • Julian Fierrez
    • 1
  • Javier Ortega-García
    • 1
  1. 1.Biometric Recognition Group - ATVS, Escuela PolitecnicaUniversidad Autonoma de MadridMadridSpain

Personalised recommendations