Abstract: The human face is, in essence, an advanced expression apparatus; despite its adverse complexity and variety of distinct expressions, researchers has concluded that at least six emotions, conveyed by human faces, are universally associated with distinct expressions. In particular, sadness, anger, joy, fear, disgust and surprise form categories of facial expressions that are recognizable across different cultures. In this work we form a description of the six universal facial expressions, using the MPEG-4 Facial Definition Parameter Set (FDP) [1]. According to the MPEG-4 Standard, this is a set of tokens that describe minimal perceptible actions in the facial area. Groups of such actions in different magnitudes produce the perception of expression [2]. A systematic approach towards the recognition and classification of such an expression is based on characteristic points in the facial area that can be automatically detected and tracked. Metrics obtained from these points feed a fuzzy inference system whose output is a vector of parameters that depicts the systems' degree of belief with respect to the observed emotion. Apart from modeling the archetypal expressions we go a step further: by modifying the membership functions of the involved features according to the activation parameter [3] we provide an efficient way for recognizing a broader range of emotions than that related with the archetypal expressions.