Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

3D face recognition based on sparse representation

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

In this paper, we present a novel 3D face recognition algorithm based on the sparse representation. First, a 3D face normalization approach is proposed to deal with the raw faces. Then, three types of facial geometrical features are extracted to describe the 3D faces. Meanwhile, in order to guarantee the feasibility of the sparse representation framework and promote the recognition efficiency, a novel feature ranking scheme based on Fisher linear discriminant analysis (FLDA) is designed to arrange the facial descriptors. Finally, the sparse representation framework is used to collect all the face features, and it addresses the recognition task. The experiments tested on the BJUT-3D and FRGC v2.0 databases demonstrate the validity of the proposed 3D face recognition algorithm, and the necessity of the FLDA ranking scheme in the sparse representation framework.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Bowyer K, Chang K, Flynn P (2006) A survey of approaches and challenges in 3d and multi-modal 3d + 2d face recognition. Comput Vis Image Underst 101(1):1–15

    Article  Google Scholar 

  3. Chang KI, Bowyer KW, Flynn PJ (2005) Adaptive rigid multi-region selection for handling expression variation in 3d face recognition. In: IEEE workshop on computer vision and pattern recognition

    Google Scholar 

  4. Samir C, Arivastava A, Daoudi M (2006) Three-dimensional face recognition using shapes of facial curves. IEEE Trans Pattern Anal Mach Intell 28(11):1858–1863

    Article  Google Scholar 

  5. Gupta S, Markey MK, Bovik AC (2007) Advances and challenges in 3d and 2d + 3d human face recognition. Nova Science Publishers, New York

    Google Scholar 

  6. Mian AS, Bennamoun M, Owens RA (2007) An efficient multimodal 2d − 3d hybrid approach to automatic face recognition. IEEE Trans Pattern Anal Mach Intell 29(11):1927–1943

    Article  Google Scholar 

  7. Mpiperis I, Malassiotis S, Strintzis MG (2008) Bilinear models for 3-d face and facial expression recognition. IEEE Trans Inform Forens Secur 3(3):498–511

    Article  Google Scholar 

  8. Mahoor MH, Abdel-Mottaleb M (2009) Face recognition based on 3d ridge images obtained from range data. Pattern Recogn 42(3):445–451

    Article  MATH  Google Scholar 

  9. Li X, Jia T, Zhang H (2009) Expression-insensitive 3d face recognition using sparse representation. In: Proc of computer vision and pattern recognition, pp 2575–2582

    Google Scholar 

  10. Tsalakanidou F, Malassiotis S (2010) Real-time 2d + 3d facial action and expression recognition. Pattern Recogn 43(5):1763–1775

    Article  Google Scholar 

  11. Candes E, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inform Theory 52(2):489–509

    Article  MathSciNet  Google Scholar 

  12. Donoho D (2006) Compressed sensing. IEEE Trans Inform Theory 52(4):1289–1306

    Article  MathSciNet  MATH  Google Scholar 

  13. Wright J, Yang A, Ganesh A, Sastry S, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  14. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  15. BJUT-3D Face Database (2005) http://www.bjpu.edu.cn/sci/multimedia/mul-lab/3dface/facedatabase.htm

  16. Phillips PJ, Flynn PJ, Scruggs T et al (2005) Overview of the face recognition grand challenge. In: Proc of computer vision and pattern recognition

    Google Scholar 

  17. Gu CL, Yin BC, Hu YL, Cheng SQ (2004) Resampling based method for pixel-wise correspondence between 3d faces. In: Proc of information technology: coding and computing, vol 1, pp 614–619

    Google Scholar 

  18. Sun YF, Tang HL, Yin BC (2008) The 3d face recognition algorithm fusing multi-geometry features. In: Acta automatica sinica, vol 34(12), pp 1483–1489

    Google Scholar 

  19. Kazarinoff MD (1961) Geometric inequalities, new math. Library, Math Assoc of America

Download references

Author information

Authors and Affiliations

  1. Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, Beijing, 100124, P.R. China

    Hengliang Tang, Yanfeng Sun, Baocai Yin & Yun Ge

Authors
  1. Hengliang Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanfeng Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Baocai Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yun Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hengliang Tang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tang, H., Sun, Y., Yin, B. et al. 3D face recognition based on sparse representation. J Supercomput 58, 84–95 (2011). https://doi.org/10.1007/s11227-010-0533-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-010-0533-9

Keywords

Search

Navigation