The spiking patterns of the neurons at different fMRI-identified face patches show a hierarchical organization of selectivity for faces in the macaque brain: neurons at the most posterior patch (ML/MF) appear to be tuned to specific view points, AL (a more anterior patch) neurons exhibit specificity to mirror-symmetric view points, and the most anterior patch (AM) appear to be largely view-invariant, i.e., neurons there show specificity to individuals (Freiwald & Tsao, 2010). This project proposes and implements a computational characterization of the macaque face patch system. Our main hypothesis is that face processing is composed of a hierarchy of processing stages where the goal is to "inverse render" a given image of a face to its underlying 3d shape and texture. The model fine-tunes and wraps a powerful feed-forward computational pipeline within a generative vision model of face shape and texture. This project aims to explain neuronal and behavioral data, and generate testable predictions about the macaque face processing system.