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Carnegie Mellon Robotics Institute

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Abstract

We develop an automatic system to analyze subtle changes in upper face expressions based on both permanent facial features (brows, eyes, mouth) and transient facial features (deepening of facial furrows) in a nearly frontal image sequence. Our system recognizes fine-grained changes in facial expression based on Facial Action Coding System (FACS) action units (AUs). Multi-state facial component models are proposed for tracting and modeling different facial features, including eyes, brews, cheeks, and furrows. Then we convert the results of tracking to detailed parametric descriptions of the facial features. These feature parameters are fed to a neural network which recognizes 7 upper face action units. A recognition rate of 95% is obtained for the test data that include both single action units and AU combinations.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Face Group

Text Reference

Ying-Li Tian, Takeo Kanade, and Jeffrey Cohn, "Recognizing upper face action units for facial expression analysis," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '00), June, 2000, pp. 294 - 301.

BibTeX Reference

@inproceedings{Tian_2000_3625,
   author = "Ying-Li Tian and Takeo Kanade and Jeffrey Cohn",
   title = "Recognizing upper face action units for facial expression analysis",
   booktitle = "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '00)",
   pages = "294 - 301",
   month = "June",
   year = "2000",
   volume = "1",
}

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