Skip to main content
SpringerLink
Log in

Illumination Robust Facial Feature Detection via Decoupled Illumination and Texture Features

  • Conference paper
  • First Online:
Image Analysis and Recognition (ICIAR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9164))

Included in the following conference series:

  • 1866 Accesses

Abstract

A method for illumination robust facial feature detection on frontal images of the human face is proposed. Illumination robust features are produced from weighted contributions of the texture and illumination components of an image where the illumination is estimated via Bayesian least-squares minimization with the required posterior probability inferred using an adaptive Monte-Carlo sampling approach. This estimate is used to decouple the illumination and texture components, from which Haar-like features are extracted. A weighted aggregate of each component’s features is then compared with a cascade of pre-trained classifiers for the face, eyes, nose, and mouth. Experimental results against the Yale Face Database B suggest higher sensitivity and \(F_1\) score values than current methods while maintaining comparable specificity and accuracy in the presence of non-ideal illumination conditions.

We would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canada Research Chairs Program, and the Ontario Ministry of Research and Innovation for their sponsorship of this research.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intel. 28(12), 2037–2041 (2006)

    Article  Google Scholar 

  2. Bartlett, M.S., Littlewort, G., Fasel, I., Movellan, J.R.: Real time face detection and facial expression recognition: Development and applications to human computer interaction. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2003, vol. 5, pp. 53–53. IEEE (2003)

    Google Scholar 

  3. Bradski, G.: Dr. Dobb’s Journal of Software Tools (2000)

    Google Scholar 

  4. Cristinacce, D., Cootes, T.F.: Facial feature detection using adaboost with shape constraints. In: BMVC, pp. 1–10. British Machine Vision Association (2003)

    Google Scholar 

  5. Elad, M.: Retinex by two bilateral filters. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds.) Scale-Space 2005. LNCS, vol. 3459, pp. 217–229. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  7. Frischholz, R.W., Dieckmann, U.: Biold: a multimodal biometric identification system. Computer 33(2), 64–68 (2000)

    Article  Google Scholar 

  8. Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intelligence 23(6), 643–660 (2001)

    Article  Google Scholar 

  9. Gourier, N., Hall, D., Crowley, J.L.: Facial features detection robust to pose, illumination and identity. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 617–622. IEEE (2004)

    Google Scholar 

  10. Hadid, A., Heikkila, J., Silvén, O., Pietikainen, M.: Face and eye detection for person authentication in mobile phones. In: First ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2007, pp. 101–108. IEEE (2007)

    Google Scholar 

  11. Hadid, A., Pietikainen, M., Ahonen, T.: A discriminative feature space for detecting and recognizing faces. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, pp. II–797. IEEE (2004)

    Google Scholar 

  12. Hu, W.C., Yang, C.Y., Huang, D.Y., Huang, C.H.: Feature-based face detection against skin-color like backgrounds with varying illumination. J. Inf. Hiding Multimedia Signal Proces. 2(2), 123–132 (2011)

    Google Scholar 

  13. Huang, D., Ardabilian, M., Wang, Y., Chen, L.: 3-D face recognition using elbp-based facial description and local feature hybrid matching. IEEE Trans. Inf. Forensics and Secur. 7(5), 1551–1565 (2012)

    Article  Google Scholar 

  14. Jobson, D.J., Rahman, Z.U., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE Trans. Image Proces. 6(3), 451–462 (1997)

    Article  Google Scholar 

  15. Lui, D., Modhafar, A., Glaister, J., Wong, A., Haider, M.: Monte Carlo bias field correction in endorectal diffusion imaging. IEEE Trans. Bio-Med. Eng. 61(2), 368–380 (2014)

    Article  Google Scholar 

  16. Morimoto, C.H., Mimica, M.R.: Eye gaze tracking techniques for interactive applications. Comput. Vis. Image Underst. 98(1), 4–24 (2005)

    Article  Google Scholar 

  17. Naruniec, J.: A survey on facial features detection. Int. J. Electr. Telecommun. 56(3), 267–272 (2010)

    Google Scholar 

  18. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recog. 29(1), 51–59 (1996)

    Article  Google Scholar 

  19. Papageorgiou, C.P., Oren, M., Poggio, T.: A general framework for object detection. In: Sixth international Conference on Computer Vision, pp. 555–562. IEEE (1998)

    Google Scholar 

  20. Roy, A., Marcel, S.: Haar local binary pattern feature for fast illumination invariant face detection. In: British Machine Vision Conference 2009. No. LIDIAP-CONF-2009-048 (2009)

    Google Scholar 

  21. Shan, C., Gong, S., McOwan, P.W.: Robust facial expression recognition using local binary patterns. In: IEEE International Conference on Image Processing, ICIP 2005, vol. 2, pp. II–370. IEEE (2005)

    Google Scholar 

  22. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I–511. IEEE (2001)

    Google Scholar 

  23. Vukadinovic, D., Pantic, M.: Fully automatic facial feature point detection using gabor feature based boosted classifiers. In: 2005 IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 1692–1698. IEEE (2005)

    Google Scholar 

  24. Wilson, P.I., Fernandez, J.: Facial feature detection using haar classifiers. J. Comput. Sci. Coll. 21(4), 127–133 (2006)

    Google Scholar 

  25. Wong, A., Clausi, D.A., Fieguth, P.: Adaptive monte carlo retinex method for illumination and reflectance separation and color image enhancement. In: Canadian Conference on Computer and Robot Vision, CRV 2009, pp. 108–115. IEEE (2009)

    Google Scholar 

  26. Zhang, H., Gao, W., Chen, X., Zhao, D.: Object detection using spatial histogram features. Image Vis. Comput. 24(4), 327–341 (2006)

    Article  Google Scholar 

  27. Zhang, L., Li, S.Z., Yuan, X., Xiang, S.: Real-time object classification in video surveillance based on appearance learning. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Waterloo, Waterloo, ON, N2L 3G1, Canada

    Brendan Chwyl, Alexander Wong & David A. Clausi

Authors
  1. Brendan Chwyl
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David A. Clausi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Brendan Chwyl .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Chwyl, B., Wong, A., Clausi, D.A. (2015). Illumination Robust Facial Feature Detection via Decoupled Illumination and Texture Features. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-20801-5_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20800-8

  • Online ISBN: 978-3-319-20801-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation