To investigate the perception of emotional facial expressions, researchers rely on shared sets of photos or videos, most often generated by actor portrayals. The drawback of such standardized material is a lack of flexibility and controllability, as it does not allow the systematic parametric manipulation of specific features of facial expressions on the one hand, and of more general properties of the facial identity (age, ethnicity, gender) on the other. To remedy this problem, we developed FACSGen: a novel tool that allows the creation of realistic synthetic 3D facial stimuli, both static and dynamic, based on the Facial Action Coding System. FACSGen provides researchers with total control over facial action units, and corresponding informational cues in 3D synthetic faces. We present four studies validating both the software and the general methodology of systematically generating controlled facial expression patterns for stimulus presentation
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