Skip to main content
SpringerLink
Log in

Optimal Regions for Linear Model-Based 3D Face Reconstruction

  • Conference paper
Computer Vision – ACCV 2010 (ACCV 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6494))

Included in the following conference series:

Abstract

In this paper, we explore region-based 3D representations of the human face. We begin by noting that although they serve as a key ingredient in many state-of-the-art 3D face reconstruction algorithms, very little research has gone into devising strategies for optimally designing them. In fact, the great majority of such models encountered in the literature is based on manual segmentations of the face into subregions. We propose algorithms that are capable of automatically finding the optimal subdivision given a training set and the number of desired regions. The generality of the segmentation approach is demonstrated on examples from the TOSCA database, and a cross-validation experiment on facial data shows that part-based models designed using the proposed algorithms are capable of outperforming alternative segmentations w.r.t. reconstruction accuracy.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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.

References

  1. Biederman, I.: Recognition-by-components: A theory of human image understanding. Psychological Review 94, 115–147 (1987)

    Article  Google Scholar 

  2. Brunelli, R., Poggio, T.: Face recognition: Features versus templates. IEEE T. Pattern Anal. 15, 1042–1052 (1993)

    Article  Google Scholar 

  3. Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: Proc. CVPR, pp. 84–91 (1994)

    Google Scholar 

  4. Martínez, A.M.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE T. Pattern Anal. 24, 748–763 (2002)

    Article  Google Scholar 

  5. Tarrés, F., Rama, A., Torres, L.: A novel method for face recognition under partial occlusion or facial expression variations. In: Proc. ELMAR, pp. 163–166 (2005)

    Google Scholar 

  6. Faltemier, T.C., Bowyer, K.W., Flynn, P.J.: A region ensemble for 3-D face recognition. IEEE T. Inf. Foren. Sec. 3, 62–73 (2008)

    Article  Google Scholar 

  7. Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Proc. SIGGRAPH, pp. 187–194 (1999)

    Google Scholar 

  8. Peyras, J., Bartoli, A., Mercier, H., Dalle, P.: Segmented AAMs improve person-independent face fitting. In: Proc. BMVC (2007)

    Google Scholar 

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

    Article  Google Scholar 

  10. Mian, A., Bennamoun, M., Owens, R.: Region-based matching for robust 3D face recognition. In: Proc. BMVC, vol. 1, pp. 199–208 (2005)

    Google Scholar 

  11. Basso, C., Verri, A.: Fitting 3D morphable models using implicit representations. In: Proc. GRAPP, pp. 45–52 (2007)

    Google Scholar 

  12. Kakadiaris, I.A., Passalis, G., Toderici, G., Murtuza, M.N., Lu, Y., Karampatziakis, N., Theoharis, T.: Three-dimensional face recognition in the presence of facial expressions: An annotated deformable model approach. IEEE T. Pattern Anal. 29, 640–649 (2007)

    Article  Google Scholar 

  13. Zhang, Y., Xu, S.: Data-driven feature-based 3D face synthesis. In: Proc. 3DIM, pp. 39–46 (2007)

    Google Scholar 

  14. ter Haar, F.B., Veltkamp, R.C.: 3D face model fitting for recognition. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 652–664. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  15. Shamir, A.: A survey on mesh segmentation techniques. Comput. Graph. Forum 27, 1539–1556 (2008)

    Article  MATH  Google Scholar 

  16. Joshi, P., Tien, W.C., Desbrun, M., Pighin, F.H.: Learning controls for blend shape based realistic facial animation. In: Proc. SIGGRAPH (2003)

    Google Scholar 

  17. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)

    Article  Google Scholar 

  18. Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5, 1457–1469 (2004)

    MathSciNet  MATH  Google Scholar 

  19. Zass, R., Shashua, A.: Nonnegative sparse PCA. In: NIPS, pp. 1561–1568 (2006)

    Google Scholar 

  20. Stauffer, C., Grimson, W.L.: Similarity templates for detection and recognition. In: Proc. CVPR, pp. 221–230 (2001)

    Google Scholar 

  21. Basso, C., Paysan, P., Vetter, T.: Registration of expressions data using a 3D morphable model. In: Proc. FG, pp. 205–210 (2006)

    Google Scholar 

  22. Kirby, M., Sirovich, L.: Application of the karhunen-loeve procedure for the characterization of human faces. IEEE T. Pattern Anal. 12, 103–108 (1990)

    Article  Google Scholar 

  23. Penev, P.S., Sirovich, L.: The global dimensionality of face space. In: Proc. FG, pp. 264–270 (2000)

    Google Scholar 

  24. Yang, Q., Ding, X.: Symmetrical PCA in face recognition. In: Proc. ICIP, vol. 2, pp. 97–100 (2002)

    Google Scholar 

  25. Arthur, D., Vassilvitskii, S.: k-means++: The advantages of careful seeding. In: Proc. SODA, pp. 1027–1035 (2007)

    Google Scholar 

  26. Bronstein, A., Bronstein, M., Kimmel, R.: Numerical Geometry of Non-Rigid Shapes. Springer Publishing Company, Incorporated, Heidelberg (2008)

    MATH  Google Scholar 

  27. Berry, M.W., Browne, M., Langville, A.N., Pauca, V.P., Plemmons, R.J.: Algorithms and applications for approximate nonnegative matrix factorization. Comput. Stat. Data An. 52, 155–173 (2007)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. K.U. Leuven ESAT-PSI/VISICS, Heverlee, Belgium

    Michaël De Smet & Luc Van Gool

Authors
  1. Michaël De Smet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luc Van Gool
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Technion – Israel Institute of Technology, Department of Computer Science, 32000, Haifa, Israel

    Ron Kimmel

  2. The University of Auckland, 37 Kohimarama Road , Mission Bay, 1071, Auckland, New Zealand

    Reinhard Klette

  3. National Institute of Informatics, Chiyoda, 1018430, Tokyo, Japan

    Akihiro Sugimoto

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

De Smet, M., Van Gool, L. (2011). Optimal Regions for Linear Model-Based 3D Face Reconstruction. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-19318-7_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19317-0

  • Online ISBN: 978-3-642-19318-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation