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Facial action tracking using particle filters and active appearance models

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sOc-EUSAI '05: Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies

Pages 165 - 169

https://doi.org/10.1145/1107548.1107592

Published: 12 October 2005 Publication History

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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.

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Cited By

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Zuhura FHossain S(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
https://doi.org/10.1063/5.0232022
Zhi RLiu MZhang D(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
https://dl.acm.org/doi/10.1007/s00371-019-01707-5
Cho DLee SSuh I(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
https://doi.org/10.5772/58761
Show More Cited By

Facial action tracking using particle filters and active appearance models
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks

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Published In

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

Conference Chairs:
Gerard Bailly
CNRS, France
,
James L. Crowley
INPG - France

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]

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Association for Computing Machinery

New York, NY, United States

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Published: 12 October 2005

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sOc-EUSAI05

sOc-EUSAI05: Smart Objects & Ambient Intelligence

October 12 - 14, 2005

Grenoble, France

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Zuhura FHossain S(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
https://doi.org/10.1063/5.0232022
Zhi RLiu MZhang D(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
https://dl.acm.org/doi/10.1007/s00371-019-01707-5
Cho DLee SSuh I(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
https://doi.org/10.5772/58761
Valstar MPantic M(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
https://dl.acm.org/doi/10.1109/TSMCB.2011.2163710
Fazli SAfrouzian RSeyedarabi H(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
https://doi.org/10.1109/ICUMT.2010.5676607
Burion SConti FPetrovskaya ABaur CKhatib O(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
https://doi.org/10.1109/ROBOT.2008.4543353
Hoffmann MSwart AHunter KHerbst BFlecky SStrasser W(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
https://doi.org/10.1109/ICDSC.2008.4635676
Choi SKim D(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
https://dl.acm.org/doi/10.5555/2391659.2391770
Sven Hoffmann MHunter KSchilling A(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
https://dl.acm.org/doi/10.1109/CRV.2007.50
Choi SKim D(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
https://doi.org/10.1007/978-3-540-74549-5_102
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A comparative study of facial appearance modeling methods for active appearance models

Emotion recognition using facial expressions with active appearance models

Face recognition across pose using view based active appearance models (VBAAMs) on CMU multi-PIE dataset

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