Skip to main content
SpringerLink
Log in

Hybrid Face Recognition Based on Real-Time Multi-camera Stereo-Matching

  • Conference paper
Advances in Visual Computing (ISVC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6939))

Included in the following conference series:

Abstract

Multi-camera systems and GPU-based stereo-matching methods allow for a real-time 3d reconstruction of faces. We use the data generated by such a 3d reconstruction for a hybrid face recognition system based on color, accuracy, and depth information. This system is structured in two subsequent phases: geometry-based data preparation and face recognition using wavelets and the AdaBoost algorithm. It requires only one reference image per person. On a data base of 500 recordings, our system achieved detection rates ranging from 95% to 97% with a false detection rate of 2% to 3%. The computation of the whole process takes around 1.1 seconds.

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 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Denker, K., Umlauf, G.: Accurate real-time multi-camera stereo-matching on the gpu for 3d reconstruction. Journal of WSCG 19, 9–16 (2011)

    Google Scholar 

  2. Pan, G., Han, S., Wu, Z., Wang, Y.: 3D face recognition using mapped depth images. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 175–181 (2005)

    Google Scholar 

  3. Turk, M., Pentland, A.: Eigenfaces for recognition. Cognitive Neuroscience 3, 71–86 (1991)

    Article  Google Scholar 

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

    Google Scholar 

  5. Blanz, V., Romdhani, S.: Face identification across different poses and illuminations with a 3d morphable model. In: Int’l. Conf. on Automatic Face and Gesture Recognition, pp. 202–2007 (2002)

    Google Scholar 

  6. Weyrauch, B., Huang, J., Heisele, B., Blanz, V.: Component-based face recognition with 3d morphable models. In: Workshop on Face Processing in Video, pp. 1–5 (2003)

    Google Scholar 

  7. Lee, Y., Song, H., Yang, U., Shin, H., Sohn, K.: Local feature based 3D face recognition. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 909–918. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  8. Lee, J., Kuo, C., Hus, C.: 3d face recognition system based on feature analysis and support vector machine. In: IEEE TENCON 2004, pp. 144–147 (2004)

    Google Scholar 

  9. Cook, J., Ch, V., Sridharan, S., Fookes, C.: Face recognition from 3d data using iterative closest point algorithm and Gaussian mixture models. In: 2nd Int’l. Symp. 3D Data Processing, Visualization, and Transmission, pp. 502–509 (2004)

    Google Scholar 

  10. Wang, J., Oliveira, M.: A hole-filling strategy for reconstruction of smooth surfaces in range images. In: SIBGRAPI 2003, pp. 11–18 (2003)

    Google Scholar 

  11. Besl, P., McKay, N.: A method for registration of 3-D shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence, 239–256 (1992)

    Google Scholar 

  12. Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: 3dim, p. 145. IEEE Computer Society, Los Alamitos (2001)

    Google Scholar 

  13. Freedman, D., Diaconis, P.: On the histogram as a density estimator: L2 theory. Probability Theory and Related Fields 57, 453–476 (1981)

    MATH  Google Scholar 

  14. Lamard, M., Cazuguel, G., Quellec, G., Bekri, L., Roux, C., Cochener, B.: Content based image retrieval based on wavelet transform coefficients distribution. In: 29th IEEE Conf. of the Engineering in Medicine and Biology Society, pp. 4532–4535 (2007)

    Google Scholar 

  15. Varanasi, M., Aazhang, B.: Parametric generalized Gaussian density estimation. J. of the Acoustical Society of America 86, 1404 (1989)

    Article  Google Scholar 

  16. Do, M., Vetterli, M.: Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE Trans. on Image Processing 11, 146–158 (2002)

    Article  MathSciNet  Google Scholar 

  17. Kullback, S., Leibler, R.: On information and sufficiency. Ann. Math. Stat. 22, 79–86 (1951)

    Article  MathSciNet  MATH  Google Scholar 

  18. Hensler, J., Blaich, M., Bittel, O.: Improved door detection fusing camera and laser rangefinder data with AdaBoosting. In: 3rd Int.’l Conf. on Agents and Artificial Intelligence, pp. 39–48 (2011)

    Google Scholar 

  19. Schapire, R.: A brief introduction to boosting. In: International Joint Conference on Artificial Intelligence, vol. 16, pp. 1401–1406 (1999)

    Google Scholar 

  20. Bowyer, K., 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 

Download references

Author information

Authors and Affiliations

  1. University of Applied Sciences, Constance, Germany

    J. Hensler, K. Denker, M. Franz & G. Umlauf

Authors
  1. J. Hensler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Denker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Franz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Umlauf
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science and Engineering, University of South Carolina, 29208, Columbia, SC, USA

    Song Wang

  6. HRL Laboratories, 3011 Malibu Canyon Road, 90265-4797, Malibu, CA, USA

    Kim Kyungnam

  7. Purdue University, West Lafayette, 47907-2021, IN, USA

    Bedrich Benes

  8. Sandia National Laboratory, 87185, Albuquerque, NM, USA

    Kenneth Moreland

  9. University of Louisiana at Lafayette, 70504, LA, USA

    Christoph Borst

  10. Adobe Systems Incorporated, San Francisco, CA, USA

    Stephen DiVerdi

  11. Polytechnic Institute of NYU, 11201, Brooklyn, NY, USA

    Chiang Yi-Jen

  12. Lawrence Livermore National Laboratory, 94551-0808, Livermore, CA, USA

    Jiang Ming

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hensler, J., Denker, K., Franz, M., Umlauf, G. (2011). Hybrid Face Recognition Based on Real-Time Multi-camera Stereo-Matching. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24031-7_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24031-7_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24030-0

  • Online ISBN: 978-3-642-24031-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation