Advertisement

Model-Based Multi-view Face Construction and Recognition in Videos

  • Chao Wang
  • Yunhong Wang
  • Zhaoxiang Zhang
  • Yiding Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7390)

Abstract

Model-based face construction and recognition in videos is a fundamental topic in image processing and video representation, while analysis faces across multiple views is more challenging than that from a fixed view because of the severe non-linearity caused by rotation in depth, self-occlusion, self-shading and illumination. In this paper, a novel method is presented to model and recognize multi-view faces in video sequences. Firstly, we design a multi-view face model to extract the face feature points. Secondly, a hybrid tracking method integrated optical flow with mean shift is proposed to estimate the face posture. Then, by using faces’ paths in different view and feature points obtained from models, a multi-view face map is synthesized by reconstruction and stitching the paths together. Finally, recognition experiments are conducted to evaluate the performance of our proposed approach.

Keywords

Face recognition Video-based face recognition Image stitching Active Appearance Model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cootes, T.F., Wheeler, G.V., Walker, K.N., Taylor, C.J.: View-based Active Appearance Models. Image and Vision Computing 20, 657–664 (2002)CrossRefGoogle Scholar
  2. 2.
    Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with An Application to Stereo vision. IJCAI 2, 674–679 (1981)Google Scholar
  3. 3.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. PAMI 25, 564–575 (2003)CrossRefGoogle Scholar
  4. 4.
    Hu, C., Harguess, J., Aggarwal, J.K.: Patch-based Face Recognition From Video. In: ICIP, pp. 3321–3324 (2009)Google Scholar
  5. 5.
    Ashraf, A.B., Lucey, S., Chen, T.: Learning Patch Correspondences for Improved Viewpoint Invariant Face Recognition. In: CVPR, pp. 1–8 (2008)Google Scholar
  6. 6.
    Viola, P., Jones, M.: Rapid Object Detection using A Boosted Cascade of Simple Features. In: CVPR, vol. 511 (2001)Google Scholar
  7. 7.
    Carnegie, R.C.: Mean-shift Blob Tracking through Scale Space. In: CVPR, vol. 2, pp. 234–240 (2003)Google Scholar
  8. 8.
    Uyttendaele, M., Eden, A., Skeliski, R.: Eliminating Ghosting And Exposure Artifacts in Image Mosaics. In: CVPR, vol. 2 (2001)Google Scholar
  9. 9.
    Gao, W., Cao, B., et al.: The Caspeal Large-scale Chinese face Database and Baseline Evaluations. IEEE Transactions on System, Man, and Cybernetics 38, 149–161 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chao Wang
    • 1
  • Yunhong Wang
    • 1
  • Zhaoxiang Zhang
    • 1
  • Yiding Wang
    • 2
  1. 1.School of Computer Science and EngineeringBeihang UniversityChina
  2. 2.School of Information EngineeringNorth China University of TechnologyChina

Personalised recommendations