One of the most challenging tasks for visual form ('shape') analysis and object recognition is the understanding of how people process and recognize each other's face, and the development of corresponding computational models. This paper describes the important and successful role learning and evolution plays on improved and robust face coding and classification schemes. In particular we describe how committees of connectionist networks and symbolic learning, evolutionary computation, and statistical learning theory lead to increased performance on tasks including the location of facial landmarks, gender and ethnic categorization, face recognition, and pose discrimination.