Study of the Pre-processing Impact in a Facial Recognition System

  • Guillermo Calvo
  • Bruno Baruque
  • Emilio Corchado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)


The present work is a study of the influence of the preprocessing stage on the classification performance of a face recognition analysis. To carry out this task have made tests in a full FRS, evaluating each of its four stages and including several advanced alternatives in preprocessing, such as illumination normalization through the Discrete Cosine Transformation or alignment by Enhanced Correlation Coefficient, among others. The main goal of this work is determining how those different preprocessing alternatives interact with each other and in wich degree they affect the overall Facial Recognition Systems (FRS). The tests make a special emphasis in using images that could have been obtained from a real environment, rather than at a lab environment, with the difficulties that this brings for facial recognicion techniques.


Face Recognition Preprocessing Normalization Alignment ECC DCT 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35, 399–459 (2003)CrossRefGoogle Scholar
  2. 2.
    Zhang, X., Gao, Y.: Face recognition across pose: A review. Pattern Recognition 42, 2876–2896 (2009)CrossRefGoogle Scholar
  3. 3.
    Sang-Il, C., Chong-Ho, C., Nojun, K.: Face recognition based on 2D images under illumination and pose variations. Pattern Recognition Letters 32, 561–571 (2011)CrossRefGoogle Scholar
  4. 4.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley Pub. (Sd) (2008)Google Scholar
  5. 5.
    Nilsson, M., Nordberg, J., Claesson, I.: Face Detection using Local SMQT Features and Split up Snow Classifier (2007)Google Scholar
  6. 6.
    Chen, W., Er, M.J., Wu, S.: Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Transactions on Systems, Man and Cybernetics, Part B (2006)Google Scholar
  7. 7.
    Štruc, V., Pavešić, N.: Photometric normalization techniques for illumination invariance. In: Advances in Face Image Analysis: Techniques and Technologies. IGI-Global (2011)Google Scholar
  8. 8.
    Štruc, V., Pavešić, N.: Gabor-based kernel-partial-least-squares discrimination features for face recognition. Informatica (Vilnius) (2009)Google Scholar
  9. 9.
    Chen, L., Grecos, C.: Fast skin color detector for face extraction. Electronic Imaging (2005)Google Scholar
  10. 10.
    Evangelidis, G.D., Psarakis, E.Z.: Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization. IEEE Transactions on Systems, Pattern Analysis and Machine Intelligence (2008)Google Scholar
  11. 11.
    Everingham, M., Sivic, J., Zisserman, A.: “Hello! My name is... Buffy” – Automatic Naming of Characters in TV Video. In: Proceedings of the British Machine Vision Conference (2006)Google Scholar
  12. 12.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience (1991)Google Scholar
  13. 13.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection (1997)Google Scholar
  14. 14.
    Miar-Naimi, H., Davari, P.: A New Fast and Efficient HMM-Based Face Recognition System Using a 7-State HMM Along With SVD Coefficients. Iranian Journal of Electrical & Electronic Engineering (2008)Google Scholar
  15. 15.
    Nilsson, M., Dahl, M., Claesson, I.: The successive mean quantization transform. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2005 (2005)Google Scholar
  16. 16.
    Rifkin, R., Klautau, A.: In Defense of One-Vs-All Classification. Journal of Machine Learning Research (2004)Google Scholar
  17. 17.
    Refaeilzadeh, P., Tang, L., Liu, H.: Cross-Validation. In: Encyclopedia of Database Systems. Springer US (2009)Google Scholar
  18. 18.
    Caltech Computational Vision Group. Faces 1999 Database, (last accessed: 2012)
  19. 19.
    AT&T Laboratories. Cambridge ORL Faces Database, (last accessed: 2012)

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guillermo Calvo
    • 1
  • Bruno Baruque
    • 1
  • Emilio Corchado
    • 2
  1. 1.Department of Civil EngineeringUniversity of BurgosSpain
  2. 2.Department of Computer Sciences and AutomaticUniversity of SalamancaSpain

Personalised recommendations