Subtly Different Facial Expression Recognition and Expression Intensity Estimation

Jenn-Jier James Lien, Takeo Kanade, Jeffrey Cohn, and Ching-Chung Li
IEEE Conference on Computer Vison and Pattern Recogntion, July, 1998, pp. 853 - 859.


Download
  • Adobe portable document format (pdf) (2MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
We have developed a computer vision system, including both facial feature extraction and recognition, that automatically discriminates among subtly different facial expressions. Expression classification is based on Facial Action Coding System (FACS) action units (AUs), and discrimination is performed using Hidden Markov Models (HMMs). Three methods are developed to extract facial expression information for automatic recognition. The first method is facial feature point tracking using a coarse-to-fine pyramid method. This method is sensitive to subtle feature motion and is capable of handling large displacements with sub-pixel accuracy. The second method is dense flow tracking together with principal component analysis (PCA) where the entire facial motion information per frame is compressed to a low-dimensional weight vector. The third method is high gradient component (i.e., furrow) analysis in the spatio-temporal domain, which exploits the transient variation associated with the facial expression. Upon extraction of the facial information, non-rigid facial expression is separated from the rigid head motion component, and the face images are automatically aligned and normalized using an affine transformation. This system also provides expression intensity estimation, which has significant effect on the actual meaning of the expression.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Associated Lab(s) / Group(s): Face Group and Component Analysis
Associated Project(s): Facial Expression Analysis and Face Databases

Text Reference
Jenn-Jier James Lien, Takeo Kanade, Jeffrey Cohn, and Ching-Chung Li, "Subtly Different Facial Expression Recognition and Expression Intensity Estimation," IEEE Conference on Computer Vison and Pattern Recogntion, July, 1998, pp. 853 - 859.

BibTeX Reference
@inproceedings{Lien_1998_1017,
   author = "Jenn-Jier James Lien and Takeo Kanade and Jeffrey Cohn and Ching-Chung Li",
   title = "Subtly Different Facial Expression Recognition and Expression Intensity Estimation",
   booktitle = "IEEE Conference on Computer Vison and Pattern Recogntion",
   pages = "853 - 859",
   month = "July",
   year = "1998",
}