FACIAL EXPRESSION ANALYSIS BY COMPUTER IMAGE PROCESSING

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Facial expression communicates information about emotions, regulates interpersonal behavior and person perception, indexes brain functioning, and is essential to evaluating preverbal infants. Current human-observer methods of facial expression analysis, however, are labor intensive and difficult to standardize across laboratories and over time. These factors force investigators to use less specific systems whose convergent validity is often unknown. To make feasible more rigorous, quantitative measurement of facial expression in diverse applications, our interdisciplinary research group, with expertise in facial expression analysis and computerized image processing, will develop automated methods of facial expression analysis. Facial expressions of emotion will be videorecorded in a directed facial action task and emotion vignettes. For each subject, a 3-D electronic wireframe face model will be created by fitting a digitized image of the subject's neutral face to a basic model. Computerized feature detection and tracking procedures will use this face model in analyzing input video image sequences. Neural pattern recognition algorithms will measure and classify facial expression action units based on measurements from the feature tracking algorithms. A user interface will permit users to define facial configurations (per EMFACS, MAX, or their own specifications) and generate time series or summary data files for immediate analysis in SPSS or other statistical software. The automated method of facial expression analysis will eliminate the need for human observers in coding facial expression (greatly reducing coding time and personnel costs), promote standardized measurement, make possible the collection and processing of larger, more representative data sets, and open new areas of investigation and clinical application. For example, comparisons between automated and human-observer methods can inform how people process emotion expressions. Unlike methods requiring skilled observation, the automated method will be readily transferable from the laboratory tot he clinic for use in diagnostics and in analysis of patient communications.