Skip to main content

Face Sample Quality

  • Reference work entry
Encyclopedia of Biometrics
  • 65 Accesses

Synonyms

Face sample standardization; Face sample utility

Definition

Face is a human biometric attribute that can be used to establish the identity of a person. A face-based biometric system operates by capturing probe face samples and comparing them against gallery face templates. The intrinsic characteristic of captured face samples determine their effectiveness for face authentication. Face sample quality is a measurement of these intrinsic characteristics. Face sample quality has significant impact on the performance of a face-based biometric system. Recognizing face samples of poor quality is a challenging problem. A number of factors can contribute toward degradation in face sample quality. They include, but not limited to, illumination variation, pose variation, facial expression change, face occlusion, low resolution, and high sensing noise.

Introduction

A typical face-based biometric system operates by capturing face data (images or videos), and comparing the obtained face...

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 449.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gao, X.F., Li, S.Z., Liu, R., Zhang, P.R.: Standardization of face image sample quality. In: Proceedings of Second International Conference on Biometrics (ICB), pp. 242–251. Seoul, Korea (2007)

    Google Scholar 

  2. Brauckmann, M., Werner, M.: Technical report. In: Proceedings of NIST Biometric Quality Workshop. (2006)

    Google Scholar 

  3. Adini, Y., Moses, Y., Ullman, S.: Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 721–732 (1997)

    Article  Google Scholar 

  4. Zhao, W., Chellappa, R.: Robust Face Recognition Using Symmetric Shape-from-Shading. Technical Report, Center for Automation Research, University of Maryland (1999)

    Google Scholar 

  5. Tarr, M.J., Bulthoff, H.H.: Image-based object recognition in man, monkey and machine. Cognition 67, 1–20 (1998)

    Article  Google Scholar 

  6. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. 35(4), 399–458(2003)

    Article  Google Scholar 

  7. Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1167–1183 (2002)

    Article  Google Scholar 

  8. ISO/IEC JTC 1/SC 37 N 1477: Biometric Sample Quality Standard – Part 1: Framework (2006)

    Google Scholar 

  9. Abdel-Mottaleb, M., Mahoor, M.H.: Application notes - algorithms for assessing the quality of facial images. IEEE Comput. Intell. Mag. 2(2), 10–17 (2007)

    Article  Google Scholar 

  10. Baker, S., Kanade, T.: Appearance characterization of linear lambertian objects, generalized photometric stereo, and illumination-invariant face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 230–245 (2007)

    Article  Google Scholar 

  11. Atick, J., Griffin, P., Redlich, A.: Statistical approach to shape from shading: reconstrunction of 3-dimensional face surfaces from single 2-dimentional images. Neural Comput. 8(2), 1321–1340 (1996)

    Article  Google Scholar 

  12. Blanz, V., Vetter, T.: Face recognition based on fitting a 3-D morphable model. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1063–1074 (2003)

    Article  Google Scholar 

  13. Li, Y., Gong, S., Liddell, H.: Constructing facial identity surfaces for recognition. Int. J. Comput. Vision 53(1), 71–92 (2003)

    Article  Google Scholar 

  14. Cootes, T.F., Walker, K., Taylor, C.J.: View-based active appearance models. In: Proceedings of Fourth International Conference on Automatic Face and Gesture Recognition (FG), pp. 227–232. Grenoble, France (2000)

    Google Scholar 

  15. Gunturk, B.K., Batur, A.U., Altunbasak, Y., Hayes, M.H., Mersereau, R.M.: Eigenface-Domain Super-Resolution for Face Recognition. IEEE Transactions on Image Processing, 12(5), 597–606 (2003)

    Article  Google Scholar 

  16. Jia, K., Gong, S.: Multi-modal tensor face for simultaneous super-resolution and recognition. In: Proceedings of Tenth International Conference on Computer Vision (ICCV), pp. 1683–1690. Beijing, China 2 (2005)

    Google Scholar 

  17. Jia, K., Gong, S.: Generalised face super-resolution. IEEE Transactions on Image Processing, 17(6), 873–886(2008).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Jia, K., Gong, S. (2009). Face Sample Quality. In: Li, S.Z., Jain, A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73003-5_86

Download citation

Publish with us

Policies and ethics