Skip to main content

Face Recognition: Shape versus Texture

  • Conference paper
Image Processing & Communications Challenges 6

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 313))

Abstract

This paper describes experiments related to the application of well-known techniques of the texture feature extraction (Local Binary Patterns and Gabor filtering) to the problem of automatic face verification. Results of the tests show that simple image normalization strategy based on the eye center detection and a regular grid of fiducial points outperforms the more complicated approach, employing active models that are able to accurately locate several landmarks. On the other hand, the proposed shape descriptor provides promising results, while the texture features appear to be very sensitive to realistic illumination changes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Choras, M., Kozik, R.: Contactless palmprint and knuckle biometrics for mobile devices. Pattern Analysis and Applications 15, 73–85 (2012)

    Article  MathSciNet  Google Scholar 

  2. Smiatacz, M., Przybycien, K.: A Framework for Training and Testing of Complex Pattern Recognition Systems. In: IEEE Conf. on Signal and Image Processing Applications (ICSIPA) (2011)

    Google Scholar 

  3. Smiatacz, M.: Eigenfaces, Fisherfaces, Laplacianfaces, Marginfaces – How to Face the Face Verification Task. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds.) CORES 2013. AISC, vol. 226, pp. 191–200. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  4. Brunelli, R., Poggio, T.: Face recognition: features versus templates. IEEE Transactions on PAMI 15, 1042–1052 (1993)

    Article  Google Scholar 

  5. Turk, M., Pentland, A.: Eigenfaces for Recognition. J. Cognitive Neuroscience 3(1), 71–86 (1990)

    Article  Google Scholar 

  6. Lades, M., Vorbruggen, J.C.: Distortion invariant object recognition in the dynamic link architecture. IEEE Computers 42, 300–310 (1993)

    Article  Google Scholar 

  7. Ojala, T., et al.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29, 51–59 (1996)

    Article  Google Scholar 

  8. Cootes, T.F., et al.: Active shape models – their training and application. Comp. Vision and Image Understanding 61, 38–59 (1995)

    Article  Google Scholar 

  9. Cootes, T.F., et al.: Active Appearance Models. IEEE Trans. PAMI 23, 681–685 (2001)

    Article  Google Scholar 

  10. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. of Computer Vision 57, 137–154 (2004)

    Article  Google Scholar 

  11. Smiatacz, M., Sikora, D.: AAM Toolkit: a system for visual object appearance modeling. In: Nguyen, N.T., Zgrzywa, A., Czyżewski, A. (eds.) Advances in Multimedia and Network Information System Technologies. AISC, vol. 80, pp. 121–129. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  12. http://www.betafaceapi.com/

  13. Phillips, P.J., et al.: The FERET evaluation methodology for face recognition algorithms. IEEE Trans. PAMI 22, 1090–1104 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maciej Smiatacz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Smiatacz, M. (2015). Face Recognition: Shape versus Texture. In: Choraś, R. (eds) Image Processing & Communications Challenges 6. Advances in Intelligent Systems and Computing, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-319-10662-5_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10662-5_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10661-8

  • Online ISBN: 978-3-319-10662-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics