Skip to main content
SpringerLink
Log in
Book cover

Intelligent Robotics and Applications pp 352–359Cite as

Robust 3D Local SIFT Features for 3D Face Recognition

  • Conference paper

  • 3526 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9246))

Abstract

In this paper, a robust 3D local SIFT feature is proposed for 3D face recognition. For preprocessing the original 3D face data, facial regional segmentation is first employed by fusing curvature characteristics and shape band mechanism. Then, we design a new local descriptor for the extracted regions, called 3D local Scale-Invariant Feature Transform (3D LSIFT). The key point detection based on 3D LSIFT can effectively reflect the geometric characteristic of 3D facial surface by encoding the gray and depth information captured by 3D face data. Then, 3D LSIFT descriptor extends to describe the discrimination on 3D faces. Experimental results based on the common international 3D face databases demonstrate the higher-qualified performance of our proposed algorithm with effectiveness, robustness, and universality.

This is a preview of subscription content, 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 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ming, Y., Ruan, Q.: A mandarin edutainment system integrated virtual learning environments. Speech Communication 55, 71–83 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Bai, X., Li, Q., Latecki, L.J., Liu, W.: Shape band: a deformable object detection approach. In: CVPR 2010, pp. 1335–1342 (2010)

    Google Scholar 

  4. Ming, Y., Ruan, Q., Hauptmann, A.: Activity recognition from kinect with 3d local spatio-temporal features. In: ICME 2012, pp. 344–349 (2012)

    Google Scholar 

  5. Ming, Y., Ruan, Q.: Robust sparse bounding sphere for 3d face recognition. Image and Vision Computing 30, 524–534 (2012)

    Article  Google Scholar 

  6. Ming, Y., Ruan, Q., Ni, R.: Learning effective features for 3d face recognition. In: ICIP 2010, pp. 2421–2424 (2010)

    Google Scholar 

  7. Moreno, A.B., Sanchez, A., Velez, J.F., Diaz, F.J.: Face recognition using 3d surface-extracted descriptors. In: IMVIP 2003 (2003)

    Google Scholar 

  8. Alyuz, N., Gokberk, B., Akarun, L.: Regional registration for expression resistant 3d face recognition. IEEE Trans. Information Forensics and Security 5, 425–440 (2010)

    Article  Google Scholar 

  9. Faltemier, T.C., Bowyer, K.W., Flynn, P.J.: A region ensemble for 3d face recognition. IEEE Trans. Information Forensics and Security 3, 62–73 (2008)

    Article  Google Scholar 

  10. Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: CVPR 2005, pp. 947–954 (2005)

    Google Scholar 

  11. Berretti, S., Bimbo, A.D., Pala, P.: 3d face recognition using isogeodesic stripes. IEEE Trans. Pattern Analysis and Machine Intelligence 32, 2162–2177 (2010)

    Article  Google Scholar 

  12. Passalis, G., Kakadiaris, I.A., Theoharis, T., Toderici, G., Murtuza, N.: Evaluation of 3d face recognition in the presence of facial expressions: an annotated deformable model approach. In: FRG 2005 (2005)

    Google Scholar 

  13. Cook, J., McCool, C., Chandran, V., Sridharan, S.: Combined 2d/3d face recognition using log-gabor templates. In: ICVSBS 2006 (2006)

    Google Scholar 

  14. 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 Trans. Pattern Analysis and Machine Intelligence 29, 640–649 (2007)

    Article  Google Scholar 

  15. Beumier, C., Acheroy, M.: Automatic 3d face authentication. Image and Vision Computing 18, 315–321 (2000)

    Article  Google Scholar 

  16. Llonch, R.S., Kokiopoulou, E., Tosic, I., Frossard, P.: 3d face recognition with sparse spherical representations. Pattern Recognition 43, 824–834 (2010)

    Article  Google Scholar 

  17. Xu, C., Li, S., Tan, T., Quan, L.: Automatic 3d face recognition from depth and intensity gabor features. Patter recognition 42, 1895–1905 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, People’s Republic of China

    Yue Ming

  2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China

    Yi Jin

Authors
  1. Yue Ming
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yue Ming .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Honghai Liu

  2. Tokyo Metropolitan University, Tokyo, Japan

    Naoyuki Kubota

  3. Shanghai Jiao Tong University, Shanghai, China

    Xiangyang Zhu

  4. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Rüdiger Dillmann

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Ming, Y., Jin, Y. (2015). Robust 3D Local SIFT Features for 3D Face Recognition. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R. (eds) Intelligent Robotics and Applications. Lecture Notes in Computer Science(), vol 9246. Springer, Cham. https://doi.org/10.1007/978-3-319-22873-0_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22873-0_31

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22872-3

  • Online ISBN: 978-3-319-22873-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation