This paper advances an integrated learning and evolutionary computation methodology for approaching the task of learning the face space. The methodology is geared to provide a framework whereby enhanced and robust face coding and classification schemes can be derived and evaluated using both machine and human benchmark studies. In particular we take an interdisciplinary approach, drawing from the accumulated and vast knowledge of both the computer vision and psychology communities, and describe how evolutionary computation and statistical learning can engage in mutually beneficial relationships in order to define an exemplar (Absolute)-Based Coding (ABC) multidimensional face space representation for successfully coping with changing population (face) types, and to leverage past experience for incremental face space definition.