In this paper we present DaFEx (Database of Facial Expressions), a database created with the purpose of providing a benchmark for the evaluation of the facial expressivity of Embodied Conversational Agents (EGAs). DaFEx consists of 1008 short videos containing emotional facial expressions of the 6 Ekman's emotions plus the neutral expression. The facial expressions were recorded by 8 professional actors (male and female) in two acting conditions ("utterance" and "no- utterance") and at 3 intensity levels (high, medium, low). The properties of DaFEx were studied by having 80 subjects classify the emotion expressed in the videos. High rates of accuracy were obtained for most of the emotions displayed. We also tested the effect of the intensity level, of the articulatory movements due to speech, and of the actors' and subjects' gender, on classification accuracy. The results showed that decoding accuracy decreases with the intensity of emotions; that the presence of articulatory movements negatively affects the recognition of fear, surprise and of the neutral expression, while it improves the recognition of anger; and that facial expressions seem to be recognized (slightly) better when acted by actresses than by actors.