Pattern Analysis for an Automatic and Low-Cost 3D Face Acquisition Technique

  • Karima Ouji
  • Mohsen Ardabilian
  • Liming Chen
  • Faouzi Ghorbel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5807)

Abstract

This paper proposes an automatic 3D face modeling and localizing technique, based on active stereovision. In the offline stage, the optical and geometrical parameters of the stereosensor are estimated. In the online acquisition stage, alternate complementary patterns are successively projected. The captured right and left images are separately analyzed in order to localize left and right primitives with sub-pixel precision. This analysis also provides us with an efficient segmentation of the informative facial region. Epipolar geometry transforms a stereo matching problem into a one-dimensional search problem. Indeed, we employ an adapted, optimized dynamic programming algorithm to pairs of primitives which are already located in each epiline. 3D geometry is retrieved by computing the intersection of optical rays coming from the pair of matched features. A pipeline of geometric modeling techniques is applied to densify the obtained 3D point cloud, and to mesh and texturize the 3D final face model. An appropriate evaluation strategy is proposed and experimental results are provided.

Keywords

Active stereovision Amplitude analysis Biometry Plastic surgery Sub-pixel sampling Dynamic programming Face segmentation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kolev, K., Cremers, D.: Integration of Multiview Stereo and Silhouettes Via Convex Functionals on Convex Domains. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 752–765. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Liu, Z., Zhang, Z., Jacobs, C., Cohen, M.: Rapid Modeling of Animated Faces From Video. Journal of Visualization and Computer Animation 12, 227–240 (2001)CrossRefMATHGoogle Scholar
  3. 3.
    D’Apuzzo, N.: Modeling human faces with multi-image photogrammetry. In: Proceedings of SPIE, San Jose, California (2002)Google Scholar
  4. 4.
    Ben Amor, B., Ardabilian, M., Chen, L.: Efficient and low-cost 2.5D and 3D face photography for recognition. In: IEEE International Conference on Signal-Image Technology and Internet-based Systems, Yaounde, Cameroun (2005)Google Scholar
  5. 5.
    Blais, F.: Review of 20 years of range sensor development. Journal of Electronic Imaging 13, 231–240 (2004)CrossRefGoogle Scholar
  6. 6.
    Ben Amor, B., Ardabilian, M., Chen, L.: An Improved 3D Human Face Reconstruction Approach Based on Cubic Splines Models. In: IEEE Int. Symposium on 3D Data Processing Visualization and Transmission, North Carolina (2006)Google Scholar
  7. 7.
    Garcia, E., Dugelay, J.L., Delingette, H.: Low Cost 3D Face Acquisition and Modeling. In: ITCC, Las Vegas (2001)Google Scholar
  8. 8.
    Zhang, L., Curless, B., Seitz, S.M.: Rapid shape acquisition using color structured light and multipass dynamic programming. In: IEEE Int. Symposium on 3D Data Processing Visualization and Transmission, Padova (2002)Google Scholar
  9. 9.
    Narasimhan, S.G., Koppal, S.J., Yamazaki, S.: Temporal Dithering of Illumination for Fast Active Vision. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 830–844. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Ohta, Y., Kanade, T.: Stereo intra- and interscanline search using dynamic programming. IEEE Trans. PAMI. 7, 139–154 (1985)CrossRefGoogle Scholar
  11. 11.
    Ouji, K., Ben Amor, B., Ardabilian, M., Chen, L., Ghorbel, F.: 3D Face Recognition using R-ICP and Geodesic Coupled Approach. In: IEEE International MultiMedia Modeling, Sophia-Antipolis (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Karima Ouji
    • 1
  • Mohsen Ardabilian
    • 1
  • Liming Chen
    • 1
  • Faouzi Ghorbel
    • 2
  1. 1.LIRIS, Lyon Research Center for Images and Intelligent Information SystemsEcole Centrale de Lyon.EcullyFrance
  2. 2.GRIFT, Groupe de Recherche en Images et Formes de TunisieEcole Nationale des Sciences de l’InformatiqueTunisie

Personalised recommendations