UC Berkeley

Previous research showed that we use different regions of a face to categorize different facial expressions, e.g. mouth region for identifying happy faces; eyebrows, eyes and upper part of nose for identifying angry faces. These findings imply that the spatial information along or close to the horizontal orientation might be more useful than others for facial expression recognition. In this study, we examined how the performance for recognizing facial expression depends on the spatial information along different orientations, and whether the pixel-level differences in the face images could account for subjects' performance. Four facial expressions-angry, fearful, happy and sad-were tested. An orientation filter (bandwidth = 23°) was applied to restrict information within the face images, with the center of the filter ranged from 0° (horizontal) to 150° in steps of 30°. Accuracy for recognizing facial expression was measured for an unfiltered and the six filtered conditions. For all four facial expressions, recognition performance (normalized d') was virtually identical for filter orientations of -30°, horizontal and 30°, and declined systematically as the filter orientation approached vertical. The information contained in mouth and eye regions is a significant predictor for subject's response (based on the confusion patterns). We conclude that young adults with normal vision categorizes facial expression most effectively based on the spatial information around the horizontal orientation which captures primary changes of facial features across expressions. Across all spatial orientations, the information contained in mouth and eye regions contributes significantly to facial expression categorization.