The face is a rich source of information about human behavior. Available methods for coding facial displays, however, are human-observer dependent, labor intensive, and difficult to standardize. To enable rigorous and efficient quantitative measurement of facial displays, we have developed an automated method of facial display analysis. In this report, we compare the results with this automated system with those of manual FACS (Facial Action Coding System, Ekman & Friesen, 1978a) coding. One hundred university students were videotaped while performing a series of facial displays. The image sequences were coded from videotape by certified FACS coders. Fifteen action units and action unit combinations that occurred a minimum of 25 times were selected for automated analysis. Facial features were automatically tracked in digitized image sequences using a hierarchical algorithm for estimating optical flow. The measurements were normalized for variation in position, orientation, and scale. The image sequences were randomly divided into a training set and a cross-validation set, and discriminant function analyses were conducted on the feature point measurements. In the training set, average agreement with manual FACS coding was 92% or higher for action units in the brow, eye, and mouth regions. In the cross-validation set, average agreement was 91%, 88%, and 81% for action units in the brow, eye, and mouth regions, respectively. Automated face analysis by feature point tracking demonstrated high concurrent validity with manual FACS coding.