Subtly Different Facial Expression Recognition and Expression Intensity Estimation

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 lowdimensional weight vector. The third method is high gradient component (i.e., furrow) analysis in the spatiotemporal domain, which exploits the transient variation associated with the faci...

• Type: Conference
• Year: 1998
• Where: CVPR
• Authors: James Jenn-Jier Lien, Takeo Kanade, Jeffrey F. Cohn, Ching-Chung Li
• Keywords: Computer Vision, CVPR 1998, Entire Facial Motion, Facial Expression Information, Facial Feature Extraction, Facial Feature Point, Non-rigid Facial Expression