Skip to main content
SpringerLink
Log in

A Unified Gradient-Based Approach for Combining ASM into AAM

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

Active Appearance Model (AAM) framework is a very useful method that can fit the shape and appearance model to the input image for various image analysis and synthesis problems. However, since the goal of the AAM fitting algorithm is to minimize the residual error between the model appearance and the input image, it often fails to accurately converge to the landmark points of the input image. To alleviate this weakness, we have combined Active Shape Models (ASM) into AAMs, in which ASMs try to find correct landmark points using the local profile model. Since the original objective function of the ASM search is not appropriate for combining these methods, we derive a gradient based iterative method by modifying the objective function of the ASM search. Then, we propose a new fitting method that combines the objective functions of both ASM and AAM into a single objective function in a gradient based optimization framework. Experimental results show that the proposed fitting method reduces the average fitting error when compared with existing fitting methods such as ASM, AAM, and Texture Constrained-ASM (TC-ASM) and improves the performance of facial expression recognition significantly.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Baker, S., Gross, R., and Matthews, I. 2003. Lucas-Kanade 20 years on: a unifying framework: part 3. CMU-RI-TR-03-05.

  • Cootes, T., Cooper, D., Taylor, C., and Graham, J. 1995. Active shape models—their training and application. Computer Vision and Image Understanding, 61(1):38–59.

    Article  Google Scholar 

  • Cootes, T., Edwards, G., and Taylor, C. 1999. Comparing active shape models with active appearance models. In British Machine Vision Conference.

  • Cootes, T., Edwards, G., and Taylor, C. 2001. Active appearance models. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(6):681–685.

    Article  Google Scholar 

  • Cootes, T. and Taylor, C. 2001. On representing edge structure for model matching. In Conference on Computer Vision and Pattern Recognition, 1:1114–1119.

  • Dornaika, F. and Ahlberg, J. 2003. Face model adaptation for tracking and active appearance model. In British Machine Vision Conference.

  • Ginneken, B., Frangi, A., Staal, J., Romeny, B., and Viergeber, M. 2002. Active shape model segmentation with optimal features. IEEE Trans. on Medical Imaging, 21(8):924–933.

    Article  Google Scholar 

  • Ginneken, B.V., Stegmann, M.B., and Loog, M. 2006. Segmentation of anatomical structures in chest radiographs using supersvised methods: A comparative study on a public database. Medical Image Analysis, 10(1):19–40.

    Article  Google Scholar 

  • Jang, J., Sun, C., and Mizutani, E. 1997. Neuro-Fuzzy and Soft Computing, Prentice Hall.

  • Kanade, T. and Lucas, B. 1981. An ierative image registration technique with an application to stereo vision. In International Joint Conference on Artificial Intelligence, 1:674–679.

  • Kass, M., Witkin, A., and Terzopoulos, D. 1998. Snakes: Active ontour models. Interntaional Journal of Computer Vision, (4):321–331.

  • Kuilenburg, H., Wiering, M., and Uyl, M. 2005. A model based method for automaic facial expression recognition. In European Conference on Maching Learning.

  • Lanitis, A., Taylor, C., and Cootes, T. 1997. Automatic interpretation and aoding of face images using flexible models. IEEE Trans. on Pattern Analysis and Maching Intelligence, 19(7):743–756.

    Article  Google Scholar 

  • Matthews, I. and Baker, S. 2004. Active appearance models revisited. International Journal of Computer Vision, 60(2):135–164.

    Article  Google Scholar 

  • Scott, I., Cootes, T., and Taylor, C. 2003. Improving appearance odel matching using local image structure. In Conference on Information Processing in Medical Imaging, 2732:258–269.

  • Stegmann, M.B. and Larsen, R. 2002. Multi-band modelling of appearance. In International Workshop on Generative Model-Based Vision.

  • Thodberg, H.H. and Rosholm, A. 2001. Application of the active shape model in a commercial medial device for bone densitometry. In British Machine Vision Conference.

  • Xiao, J., Baker, S., Matthews, I., and Kanade, T. 2004. Real-time combined 2D + 3D active appearance models. In International Conference on Computer Vision and Pattern Recognition.

  • Yan, S., Liu, C., Li, S., Zhang, H., Shum, H., and Cheng, Q. 2002. Texture-constrained active shape models. In European Conference on Computer Vision.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, POSTECH, San 31, Hyoja-Dong, Nam-Gu, Pohang, 790-784, Korea

    Jaewon Sung & Daijin Kim

  2. Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Takeo Kanade

Authors
  1. Jaewon Sung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Takeo Kanade
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daijin Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daijin Kim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sung, J., Kanade, T. & Kim, D. A Unified Gradient-Based Approach for Combining ASM into AAM. Int J Comput Vis 75, 297–309 (2007). https://doi.org/10.1007/s11263-006-0034-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11263-006-0034-8

Keywords

Search

Navigation