Automatically Recognizing Facial Expressions in the Spatio-Temporal Domain
James Jenn-Jier Lien, Takeo Kanade, Adena J. Zlochower, Jeffrey F. Cohn, Ching-Chung Li · 1999
We developed a computer vision system that automatically recognizes facial action units (AUs) or AU combinations using Hidden Markov Models (HMMs). AUs are defined as visually discriminable muscle movements. The facial expressions are recognized in digitized image sequences of arbitrary length. In this paper, we use two approaches to extract the expression information: (1) facial feature point tracking, which is sensitive to subtle feature motion, in the mouth region, and (2) pixel-wise flow tracking, which includes more motion information, in the forehead and brow regions. In the latter approach, we use principal component analysis (PCA) to compress the data. We accurately recognize 93% of the lower face expressions and 91% of the upper face expressions. 1. Introduction Facial expression provides cues about emotion and regulates interpersonal interaction. Because of its relevance to the study of psychological phenomena and the development of human-computer interaction (HCI), automat...