Advertisement

Active Shape Model and Its Application to Face Alignment

  • Huchuan Lu
  • Fan Yang
Part of the Studies in Computational Intelligence book series (SCI, volume 552)

Abstract

Active Shape Model (ASM) is a model-based method, which makes use of a prior model of what is expected in the image, and typically attempts to find the best match position between the model and the data in a new image. It has been successfully applied to many problems and we apply ASM to the face recognition. We represent all shapes with a set of landmarks to form a Point Distribution Model (PDM) respectively. After landmarks alignment and Principal Component Analysis, we construct gray-level profile for each landmark in all multi-resolution versions of a training image. In search procedure, we give the model’s position an initial estimate. We adopt a lot of improvements to the classical ASM, such as increasing the width of search profile to reduce the effect of noise, grouping landmarks to avoid mouth shape distort in the search procedure and altering the direction of search profile.

Keywords

Training Image Kernel Principal Component Analysis Face Shape Landmark Point Active Shape Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cootes, T.F., Taylor, C.J.: Statistical Models of Appearance for Computer Vision. Technical Report (2004)Google Scholar
  2. 2.
    Cootes, T.F., Taylor, C.J.: Active Shape Models - ‘Smart Snakes’. In: Proc. British Machine Vision Conference, pp. 266–275 (1992)Google Scholar
  3. 3.
    Cootes, T.F., Taylor, C.J., Cooper, D., Graham, J.: Active Shape Models – Their Training and Application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  4. 4.
    Hamarneh, G.: Active Shape Models, Modeling Shape Variations and Gray Level Information and an Application to Image Search and Classification. Technical Report R005/1998 (S2-IAG-98-1). Chalmers University of Technology, Sweden (1998)Google Scholar
  5. 5.
    Lanitis, A., Taylor, C.J., Cootes, T.F.: A Unified Approach to Coding and Interpretting Faces. In: Proc. 5th International Conference on Computer Vision, pp. 368–373 (1995)Google Scholar
  6. 6.
    Lanitis, A., Taylor, C.J., Cootes, T.F.: Automatic Interpretation and Coding of Face Images Using Flexible Models. IEEE PAMI 19(7), 743–756 (1997)CrossRefGoogle Scholar
  7. 7.
    Cootes, T.F., Page, G.J., Jackson, C.B., Taylor, C.J.: Statistical Grey-Level Models for Object Location and Identification. Image and Vision Computing 14(8), 533–540 (1996)CrossRefGoogle Scholar
  8. 8.
    Cootes, T.F., Taylor, C.J.: Using Grey-Level Models to Improve Active Shape Model Search. In: Proc. International Conference on Pattern Recognition, vol. 1, pp. 63–67 (1994)Google Scholar
  9. 9.
    Cootes, T.F., Taylor, C.J., Lanitis, A.: Multi-Resolution Search with Active Shape Models. In: Proc. International Conference on Pattern Recognition, vol. 1, pp. 610–612 (1994)Google Scholar
  10. 10.
    Cootes, T.F., Taylor, C.J., Lanitis, A.: Active Shape Models: Evaluation of a Multi-Resolution Method for Improving Image Search. In: Proc. the British Machine Vision Conference, pp. 327–336 (1994)Google Scholar
  11. 11.
    Edwards, G.J., Lanitis, A., Taylor, C.J., Cootes, T.F.: Face recognition using statistical models. IEE Colloquium on Image Processing for Security Applications, No. 1997/074, 2/1-2/6 (1997)Google Scholar
  12. 12.
    Hill, A., Cootes, T.F., Taylor, C.J.: Active Shape Models and the shape approximation problem. Image and Vision Computing 14(8), 601–608 (1996)CrossRefGoogle Scholar
  13. 13.
    Cootes, T.F., Edwards, G., Taylor, C.J.: Comparing Active Shape Models with Active Appearance Models. In: Proc. the British Machine Vision Conference, pp. 173–182 (1999)Google Scholar
  14. 14.
    Lu, H., Shi, W.: Accurate Active Shape Model for Face Alignment. In: 17th IEEE International Conference on Tools with Artificial Intelligence, pp.642–646 (2005)Google Scholar
  15. 15.
    Yang, J., Lu, W., Waibel, A.: Skin-color Modeling and Adaptation. In: Chin, R., Pong, T.-C. (eds.) ACCV 1998. LNCS, vol. 1352, pp. 687–694. Springer, Heidelberg (1997)Google Scholar
  16. 16.
    Yang, J., Waibel, A.: A Real-time Face Tracker: Applications of Computer Vision. In: Proc. 3rd IEEE Workshop on WAC 1996, pp. 142–147 (1996)Google Scholar
  17. 17.
    van Ginneken, B., Frangi, A.F., Staal, J.J., ter Haar Romeny, B.M., Viergever, M.A.: A non-linear gray-level appearance model improves active shape model segmentation. In: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 205–212 (2001)Google Scholar
  18. 18.
    Zhang, L., Ai, H.: Multi-View Active Shape Model with Robust Parameter Estimation. In: 18th International Conference on Pattern Recognition, pp. 465–468 (2006)Google Scholar
  19. 19.
    Xin, S., Ai, H.: Face Alignment under Various Poses and Expressions. In: Proc. SPIE, vol. 5286(558), pp. 40–47 (2003)Google Scholar
  20. 20.
    van Ginneken, B., Frangi, A.F., Staal, J.J., ter Haar Romeny, B.M., Viergever, M.A.: Active Shape Model Segmentation with Optimal Features. IEEE Trans. on Medical Imaging 21(8), 924–933 (2002)CrossRefGoogle Scholar
  21. 21.
    Ordas, S., Boisrobert, L., Huguet, M., Frangi, A.F.: Active Shape Models with Invariant Optimal Features (IOF-ASM) Application to Cardiac MRI Segmentation. Computers in Cardiology, 633–636 (2003)Google Scholar
  22. 22.
    Yan, S., Liu, C., Li, S.Z., Zhang, H., Shum, H.-Y., Cheng, Q.: Face Alignment Using Texture-constrained Active Shape Models. Image and Vision Computing 21, 69–75 (2003)CrossRefGoogle Scholar
  23. 23.
    Wang, L., Ding, X., Fang, C.: Generic Face Alignment Using an Improved Active Shape Model. In: International Conference on Audio, Language and Image Processing, pp. 317–321 (2008)Google Scholar
  24. 24.
    Romdhani, S., Gong, S., Psarrou, A.: A Multi-View Nonlinear Active Shape Model Using Kernel PCA. In: Proc. the British Machine Vision Conference, pp. 483–492 (1999)Google Scholar
  25. 25.
    Hamarneh, G., Gustavsson, T.: Deformable Spatio-temporal Shape Models: Extending Active Shape Models to 2D+time. Image and Vision Computing 22, 461–470 (2004)CrossRefGoogle Scholar
  26. 26.
    Seshadri, K., Savvides, M.: Robust modified Active Shape Model for automatic facial landmark annotation of frontal faces. Biometrics: Theory, Applications, and Systems. In: IEEE 3rd International Conference on BTAS, pp. 1–8 (2009)Google Scholar
  27. 27.
    Xiong, P., Huang, L., Liu, C.: Initialization and Pose Alignment in Active Shape Model. In: 20th International Conference on Pattern Recognition, pp. 3971–3974 (2010)Google Scholar
  28. 28.
    Chen, S.Y., Zhang, J., Guan, Q., Liu, S.: Detection and amendment of shape distortions based on moment invariants for active shape models. IET Image Processing 5(3), 273–285 (2011)CrossRefGoogle Scholar
  29. 29.
    Sun, S., Bauer, C., Beichel, R.: Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach. IEEE Trans. on Medical Imaging 31(2), 449–460 (2011)Google Scholar
  30. 30.
    Lee, Y.-H., Yang, D.-S., Lim, J.-K., Lee, Y., Kim, B.: Improved Active Shape Model for Efficient Extraction of Facial Feature Points on Mobile Devices. In: Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 256–259 (2013)Google Scholar
  31. 31.
    Lekadir, K., Merrifield, R., Yang, G.-Z.: Outlier Detection and Handling for Robust 3-D Active Shape Models Search. IEEE Trans. on Medical Imaging 26(2), 212–222 (2007)CrossRefGoogle Scholar
  32. 32.
    Tong, Y., Wang, Y., Zhu, Z., Ji, Q.: Robust Facial Feature Tracking under Varying Face Pose and Facial Expression. Pattern Recognition 40, 3195–3208 (2007)CrossRefzbMATHGoogle Scholar
  33. 33.
    Cootes, T.F., Hill, A., Taylor, C.J., Haslam, J.: The Use of Active Shape Models for Locating Structures in Medical Images. Image and Vision Computing 12, 355–366 (1994)CrossRefGoogle Scholar
  34. 34.
    Zhao, Z., Teoh, E.K.: Robust MR Image Segmentation Using 3D Partitioned Active Shape Models. In: International Conference on Control, Automation, Robotics and Vision, pp. 1–6 (2006)Google Scholar
  35. 35.
    Koutaki, G., Uchimura, K., Hu, Z.: Network Active Shape Model for Updating Road Map from Aerial Images. In: IEEE Intelligent Vehicles Symposium, pp. 325–330 (2006)Google Scholar
  36. 36.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET Evaluation Methodology for Face Recognition Algorithms. IEEE PAMI 22, 1090–1104 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Huchuan Lu
    • 1
  • Fan Yang
    • 2
  1. 1.School of Electronic and Information EngineeringDalian University of TechnologyDalianChina
  2. 2.Department of Computer ScienceUniversity of MarylandCollege ParkUSA

Personalised recommendations