The McGill Face Database: validation and insights into the recognition of facial expressions of complex mental states

Current databases of facial expressions of mental states typically represent only a small subset of expressions, usually covering the basic emotions (fear, disgust, surprise, happiness, sadness, and anger). To overcome these limitations, we introduce a new database of pictures of facial expressions reflecting the richness of mental states. 93 expressions of mental states were interpreted by two professional actors and high-quality pictures were taken under controlled conditions in front and side view. The database was validated with two different experiments (N=65). First, a four-alternative forced choice paradigm was employed to test the ability of participants to correctly select a term associated with each expression. In a second experiment, we employed a paradigm that did not rely on any semantic information. The task was to locate each face within a two-dimensional space of valence and arousal (mental state – space) employing a “point-and-click” paradigm. Results from both experiments demonstrate that subjects can reliably recognize a great diversity of emotional states from facial expressions. Interestingly, while subjects’ performance was better for front view images, the advantage over the side view was not dramatic. To our knowledge, this is the first demonstration of the high degree of accuracy human viewers exhibit when identifying complex mental states from only partially visible facial features. The McGill Face Database provides a wide range of facial expressions that can be linked to mental state terms and can be accurately characterized in terms of arousal and valence.