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Abstract

Previous work suggests that Gabor-wavelet-based methods can achieve high sensitivity and specificity for emotion-specified expressions (e.g., happy, sad) and single action units (AUs) of the Facial Action Coding System (FACS). This paper evaluates a Gabor-wavelet-based method to recognize AUs in image sequences of increasing complexity. A recognition rate of 83% is obtained for three single AUs when image sequences contain homogeneous subjects and are without observable head motion. The accuracy of AU recognition decreases to 32% when the number of AUs increases to nine and the image sequences consist of AU combinations, head motion, and non-homogeneous subjects. For comparison, an average recognition rate of 87.6% is achieved for the geometry-feature-based method. The best recognition is a rate of 92.7% obtained by combining Gabor wavelets and geometry features.

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, "Evaluation of Gabor-wavelet-based facial action unit recognition in image sequences of increasing complexity," Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, May, 2002, pp. 229 - 234.

BibTeX Reference

@inproceedings{Tian_2002_4126,
   author = "Ying-Li Tian and Takeo Kanade and Jeffrey Cohn",
   title = "Evaluation of Gabor-wavelet-based facial action unit recognition in image sequences of increasing complexity",
   booktitle = "Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition",
   pages = "229 - 234",
   month = "May",
   year = "2002",
}

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