In this paper we present an evaluation study for 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 (ECAs). DaPEx 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 "non 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. We tested the effect of the intensity level, of the articulatory movements due to speech, and of the actors' and subjects' gender, on classification accuracy. We also studied the way error distribute across confusion classes. The results are summarized in this work.