3 Recent Advances in Automatic Facial Expression Analysis

For automatic facial expression analysis, Suwa et al. [76] presented an early attempt in 1978 to analyze facial expressions by tracking the motion of 20 identified spots on an image sequence. Considerable progress had been made since 1990 in related technologies such as image analysis and pattern recognition that make AFEA possible. Samal and Iyengar [69] surveyed the early work (before 1990) about automatic recognition and analysis of human face and facial expression. Recently, two survey papers summarized the work (before year 1999) of facial expression analysis [31, 59]. In this chapter, instead of giving a comprehensive survey of facial expression analysis literature, we explore the recent advances in facial expression analysis based on three problems: (1) face acquisition, (2) facial feature extraction and representation, and (3) facial expression recognition.

The most extensive work about facial expression analysis includes the systems of CMU (Carnegie Mellon University) [81, 82,14, 57, 90], UCSD (University of California, San Diego) [3, 20, 33], UIUC (University of Illinois at Urbana-Champaign) [13, 88], MIT (Massachusetts Institute of Technology) [28], UMD (University of Maryland) [7, 93], TUDELFT (Delft University of Technology) [60, 59], IDIAP (Dalle Molle Institute for Perceptual Artificial Intelligence) [30, 31], and others [52, 97]. Because most of the work of MIT, UMD, TUDELFT, IDIAP, and others are summarized in the survey papers [31, 59], here we summarize the systems of CMU, UCSD, and UIUC. We focus on the new developments after 2000. The recent research in...