Skip to main content

Real-Time 3D Face Acquisition Using Reconfigurable Hybrid Architecture


Acquiring 3D data of human face is a general problem which can be applied in face recognition, virtual reality, and many other applications. It can be solved using stereovision. This technique consists in acquiring data in three dimensions from two cameras. The aim is to implement an algorithmic chain which makes it possible to obtain a three-dimensional space from two two-dimensional spaces: two images coming from the two cameras. Several implementations have already been considered. We propose a new simple real-time implementation based on a hybrid architecture (FPGA-DSP), allowing to consider an embedded and reconfigurable processing. Then we show our method which provides depth map of face, dense and reliable, and which can be implemented on an embedded architecture. A various architecture study led us to a judicious choice allowing to obtain the desired result. The real-time data processing is implemented in an embedded architecture. We obtain a dense face disparity map, precise enough for considered applications (multimedia, virtual worlds, biometrics) and using a reliable method.



  1. 1.

    Beumier C, Acheroy M: Automatic face verification from 3D and grey level clues. Proceedings of the 11th Portuguese Conference on Pattern Recognition (RECPAD '00), May 2000, Porto, Portugal 95-101.

    Google Scholar 

  2. 2.

    Jebara TS, Pentland A: Parametrized structure from motion for 3D adaptive feedback tracking of faces. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '97), June 1997, San Juan, Puerto Rico, USA 144-150.

    Google Scholar 

  3. 3.

    Cartoux JY: Formes dans les images de profondeur. Application à la reconnaissance et à l'authentification de visages, Ph.D. thesis. Université Blaise Pascal, Clermont-Ferrand Cedex, France; 1989.

    Google Scholar 

  4. 4.

    Turk M, Pentland A: Eigenfaces for recognition. Journal of Cognitive Neuroscience 1991,3(1):71-86. 10.1162/jocn.1991.3.1.71

    Article  Google Scholar 

  5. 5.

    Devernay F: Vision stéréoscopique et propriétés différentielles des surfaces, Ph.D. thesis. Ecole Polytechnique, l'Institut National de Recherche en Informatique et en Automatique, Chesnay Cedex, France; 1997.

    Google Scholar 

  6. 6.

    Faugeras O, Hotz B, Mathieu H, et al.: Real time correlation based stereo: algorithm implementations and applications. In Tech. Rep. RR-2013. l'Institut National de Recherche en Informatique et en Automatique, Chesnay Cedex, France; 1993.

    Google Scholar 

  7. 7.

    Ohm J-R, Izquierdo EM: An object-based system for stereoscopic viewpoint synthesis. IEEE Transactions on Circuits and Systems for Video Technology 1997,7(5):801-811. 10.1109/76.633502

    Article  Google Scholar 

  8. 8.

    Porr B, Cozzi A, Wörgötter F: How to "hear" visual disparities: real-time stereoscopic spatial depth analysis using temporal resonance. Biological Cybernetics 1998,78(5):329-336. 10.1007/s004220050437

    Article  MATH  Google Scholar 

  9. 9.

    Koschan A: What is new in computational stereo since 1989: a survey of current stereo papers. In Technischer Bericht 93-22. Technische Universiteät Berlin, Berlin, Germany; 1993.

    Google Scholar 

  10. 10.

    Dhond UR, Aggarwal JK: Structure from stereo—a review. IEEE Transactions on Systems, Man and Cybernetics 1989,19(6):1489-1510. 10.1109/21.44067

    MathSciNet  Article  Google Scholar 

  11. 11.

    Alvarez L, Deriche R, Sanchez J, Weickert J: Dense disparity map estimation respecting image discontinuities. In Tech. Rep. 3874. l'Institut National de Recherche en Informatique et en Automatique, Chesnay Cedex, France; 2000.

    Google Scholar 

  12. 12.

    Jenkin MRM, Jepson AD: Recovering local surface structure through local phase difference measurements. CVGIP: Image Understanding 1994,59(1):72-93. 10.1006/ciun.1994.1005

    Article  Google Scholar 

  13. 13.

    Fua P: A parallel stereo algorithm that produces dense depth maps and preserves image features. Machine Vision and Applications 1993,6(1):35-49. 10.1007/BF01212430

    Article  Google Scholar 

  14. 14.

    Deriche R, Faugeras O: Les EDP en traitement des images et vision par ordinateur. Traitement du Signal 1996.,13(6):

  15. 15.

    Fusiello A, Roberto V, Trucco E: Efficient stereo with multiple windowing. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '97), June 1997, San Juan, Puerto Rico, USA 858-863.

    Google Scholar 

  16. 16.

    Maimone MW, Shafer SA: Modeling foreshortening in stereo vision using local spatial frequency. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '95), August 1995, Pittsburgh, Pa, USA 1: 519-524.

    Google Scholar 

  17. 17.

    Hoey J: Stereo disparity from local image phase. University of British Columbia, Vancouver, British Columbia, Canada; 1999.

    Google Scholar 

  18. 18.

    Ohzawa I, DeAngelis GC, Freeman RD: The neural coding of stereoscopic depth. NeuroReport 1997,8(3):3-12.

    Google Scholar 

  19. 19.

    Churchland P, Sejnowski T: The Computational Brain. MIT Press, Cambridge, Mass, USA; 1992.

    Google Scholar 

  20. 20.

    Kingsbury N: Image processing with complex wavelets. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 1999,357(1760):2543-2560. on a discussion meeting on "wavelets: the key to intermittent information?", London, UK, February 1999 10.1098/rsta.1999.0447

    Article  MATH  Google Scholar 

  21. 21.

    Pan H, Magarey J: Phase-based bidirectional stereo in coping with discontinuity and occlusion. Proceedings of International Workshop on Image Analysis and Information Fusion, November 1997, Adelaide, South Australia 239-250.

    Google Scholar 

  22. 22.

    Magarey J, Dick A, Brooks P, Newsam GN, van den Hengel A: Incorporating the epipolar constraint into a multiresolution algorithm for stereo image matching. Proceedings of the 17th IASTED International Conference on Applied Informatics, February 1999, Innsbruck, Austria 600-603.

    Google Scholar 

  23. 23.

    Zimmer J-P: Modélisation de visage en temps réel par stéréovision, Thesis. University of Burgundy, Dijon, France, 2000.

    Google Scholar 

  24. 24.

    Zhang Z: Determining the epipolar geometry and its uncertainty: a review. International Journal of Computer Vision 1998,27(2):161-195. 10.1023/A:1007941100561

    Article  Google Scholar 

  25. 25.

    Hartley RI: Theory and practice of projective rectification. International Journal of Computer Vision 1999,35(2):115-127. 10.1023/A:1008115206617

    Article  Google Scholar 

  26. 26.

    Zimmer J-P, Mitéran J, Yang F, Paindavoine M: Security software using neural networks. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (IECON '98), August-September 1998, Aachen, Germany 1: 72-74.

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Johel Mitéran.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Mitéran, J., Zimmer, J., Paindavoine, M. et al. Real-Time 3D Face Acquisition Using Reconfigurable Hybrid Architecture. J Image Video Proc 2007, 081387 (2007).

Download citation


  • Virtual Reality
  • Face Recognition
  • Virtual World
  • Human Face
  • Judicious Choice