Data collected through a recent web-based survey show that the perception (i.e. labeling) of a human facial expression by a human observer is a subjective process, which results in a lack of a unique ground-truth, as intended in the standard classification framework. In this paper we propose the use of Discrete Choice Models(DCM) for human perception of static facial expressions. Random utility functions are defined in order to capture the attractiveness, perceived by the human observer for an expression class, when asked to assign a label to an actual expression image. The utilities represent a natural way for the modeler to formalize her prior knowledge on the process. Starting with a model based on Facial Action Coding Systems (FACS), we subsequently defines two other models by adding two new sets of explanatory variables. The model parameters are learned through maximum likelihood estimation and a cross-validation procedure is used for validation purposes.