Skip to main content

Face Misalignment Problem

  • Reference work entry
  • First Online:
Encyclopedia of Biometrics

Synonyms

Curse of misalignment; Face alignment error; Localization inaccuracy

Definition

The face misalignment problem, or curse of misalignment, means abrupt degradation of recognition performance due to possible inaccuracy in automatic localization of facial landmarks (such as the eye centers) in the face recognition process. Because these landmarks are generally used for aligning faces, inaccurate landmark positions imply incorrect semantic alignment between the faces or features, which can further result in matching or classification errors. Since perfect alignment is often very difficult, face recognition should be misalignment-robust, i.e., it should work well even if the landmarks are inaccurately located. To achieve this, there are three possible solutions: misalignment-invariant features, misalignment modeling, and alignment retuning.

Introduction

In face recognition, before extracting features from a face image, it must be aligned properly with either the reference faces or a...

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 899.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover 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. S. Shan, Y. Chang, W. Gao, B. Cao, Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution, in Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, 17–19 May 2004, pp. 314–320

    Google Scholar 

  2. P.N. Belhumeur, J.P. Hespanha et al., Eigenfaces vs Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 20(7), 711–720 (1997)

    Google Scholar 

  3. S. Shan, W. Gao, Y. Chang, B. Cao, P. Yang, Review the strength of Gabor features for face recognition from the angle of its robustness to mis-alignment, in Proceedings of 17th International Conference on Pattern Recognition (ICPR2004), Cambridge, vo1. I, 23–26 Aug 2004, pp. 338–341

    Google Scholar 

  4. T. Ahonen, A. Hadid, M. Pietikainen, Face recognition with local binary patterns, in Eighth European Conference on Computer Vision, Prague, May 2004, pp. 469–481

    Google Scholar 

  5. W. Zhang, S. Shan, W. Gao, X. Chen, H. Zhang, Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition, in Tenth IEEE International Conference on Computer Vision, Beijing, 17–20 Oct 2005, pp. 786–791

    Google Scholar 

  6. A.M. Martinez, Recognizing imprecisely localized, partially occluded and expression variant faces from a single sample per class. IEEE Trans. Pattern. Anal. Mach. Intell. 24(6), 748–763 (2002)

    Google Scholar 

  7. H. Wang, S. Yan, T. Huang, J. Liu, X. Tang, Misalignment-Robust face recognition, in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2008, Alaska, 24–26 June 2008

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this entry

Cite this entry

Shan, S., Chen, X., Gao, W. (2015). Face Misalignment Problem. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_376

Download citation

Publish with us

Policies and ethics