skip to main content
10.1145/1107548.1107592acmotherconferencesArticle/Chapter ViewAbstractPublication Pagessoc-eusaiConference Proceedingsconference-collections
Article

Facial action tracking using particle filters and active appearance models

Published: 12 October 2005 Publication History

Abstract

Tracking a face and its facial features in a video sequence is a challenging problem in computer vision. In this view, we propose a stochastic tracking system based on a particle- filtering scheme. In this paradigm, the unobserved state includes global face pose and appearance parameters coding both shape and texture information of the face. The adopted observations distribution is derived from an Active Appearance Model (AAM). The transition distribution and the particles number are adaptive in the sense that they are guided by an AAM deterministic search. This optimization stage adjusts the explored area of the state space to the quality of the prediction and enables a substantial gain in computing time. The observation model uses a robust distance measure in order to account for occlusions. Experiments on real video show encouraging results.

References

[1]
S. Baker and I. Matthews. Lucas-kanade 20 years on: A unifying framework. Int. Journal of Computer Vision, 56(3):221--255, February 2004.
[2]
F. Bettinger, T. F. Cootes, and C. J. Taylor. Modelling facial behaviours. In Proc. BMVC 2002, volume 2, pages 797--806, 2002.
[3]
M. Black and A. Jepson. Eigen-tracking: Robust matching and tracking of articulated objects using a view-based representation. Int. Journal of Computer Vision, 36(2): 101--130, 1998.
[4]
T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active appearance models. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(6):681--685, June 2001.
[5]
A. Doucet, J. F. G. De Freitas, and N. Gordon. Sequential Monte Carlo Methods in Practice. Springer-Verlag, 2001.
[6]
P. J. Huber. Robust statistics. Wiley, 1981.
[7]
M. Isard and A. Blake. Condensation - conditional density propagation for visual tracking. Int. Journal of Computer Vision, 29(1):5--28, 1998.
[8]
P. Pérez, C. Hue, J. Vermaak, and M. Gangnet. Colorbased probabilistic tracking. In Proc. Europ. Conf. Computer Vision, pages 661--675, 2002.
[9]
D. Ross, J. Lim, and M.-H. Yang. Adaptive probabilistic visual tracking with incremental subspace update. In Proceedings of the European Conference on Computer Vision, 2004.
[10]
S. Zhou, R. Chellappa, and B. Moghaddam. Visual tracking and recognition using appearance-adaptive models in particle Iters. IEEE Trans. on Image Processing, To appear, 2004.

Cited By

View all
  • (2024)A framework for real-time orientation detectionTHE 1ST INTERNATIONAL CONFERENCE ON INNOVATIONS IN ENGINEERING, SCIENCE AND TECHNOLOGY FOR SUSTAINABLE DEVELOPMENT (ICEST 2023)10.1063/5.0232022(050002)Online publication date: 2024
  • (2020)A comprehensive survey on automatic facial action unit analysisThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-019-01707-536:5(1067-1093)Online publication date: 1-May-2020
  • (2014)Facial Feature Tracking Using Efficient Particle Filter and Active Appearance ModelInternational Journal of Advanced Robotic Systems10.5772/5876111:9Online publication date: 1-Jan-2014
  • Show More Cited By
  1. Facial action tracking using particle filters and active appearance models

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      sOc-EUSAI '05: Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
      October 2005
      316 pages
      ISBN:1595933042
      DOI:10.1145/1107548
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 12 October 2005

      Permissions

      Request permissions for this article.

      Check for updates

      Qualifiers

      • Article

      Conference

      sOc-EUSAI05
      sOc-EUSAI05: Smart Objects & Ambient Intelligence
      October 12 - 14, 2005
      Grenoble, France

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 01 Mar 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)A framework for real-time orientation detectionTHE 1ST INTERNATIONAL CONFERENCE ON INNOVATIONS IN ENGINEERING, SCIENCE AND TECHNOLOGY FOR SUSTAINABLE DEVELOPMENT (ICEST 2023)10.1063/5.0232022(050002)Online publication date: 2024
      • (2020)A comprehensive survey on automatic facial action unit analysisThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-019-01707-536:5(1067-1093)Online publication date: 1-May-2020
      • (2014)Facial Feature Tracking Using Efficient Particle Filter and Active Appearance ModelInternational Journal of Advanced Robotic Systems10.5772/5876111:9Online publication date: 1-Jan-2014
      • (2012)Fully Automatic Recognition of the Temporal Phases of Facial ActionsIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics10.1109/TSMCB.2011.216371042:1(28-43)Online publication date: 1-Feb-2012
      • (2010)Fiducial facial points tracking using particle filter and geometric featuresInternational Congress on Ultra Modern Telecommunications and Control Systems10.1109/ICUMT.2010.5676607(396-400)Online publication date: Oct-2010
      • (2008)Identifying physical properties of deformable objects by using particle filters2008 IEEE International Conference on Robotics and Automation10.1109/ROBOT.2008.4543353(1112-1117)Online publication date: May-2008
      • (2008)Model-based robust and pecise tracking embedded in smart cameras—the PFAAM-CAM2008 Second ACM/IEEE International Conference on Distributed Smart Cameras10.1109/ICDSC.2008.4635676(1-8)Online publication date: Sep-2008
      • (2007)Multiple faces tracking using motion prediction and IPCA in particle filtersProceedings of the 2007 international conference on Advances in Biometrics10.5555/2391659.2391770(978-987)Online publication date: 27-Aug-2007
      • (2007)PFAAM An Active Appearance Model based Particle Filter for both Robust and Precise TrackingProceedings of the Fourth Canadian Conference on Computer and Robot Vision10.1109/CRV.2007.50(339-346)Online publication date: 28-May-2007
      • (2007)Multiple Faces Tracking Using Motion Prediction and IPCA in Particle FiltersAdvances in Biometrics10.1007/978-3-540-74549-5_102(978-987)Online publication date: 2007
      • Show More Cited By

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media