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Ample evidence suggests that face processing in human and non-human primatesis performed differently compared with other objects. Converging reports, bothphysiologically and psychophysically, indicate that faces are processed inspecialized neural networks in the brain -i.e. face patches in monkeys and thefusiform face area (FFA) in humans. We are all expert face-processing agents,and able to identify very subtle differences within the category of faces,despite substantial visual and featural similarities. Identification isperformed rapidly and accurately after viewing a whole face, whilesignificantly drops if some of the face configurations (e.g. inversion,misalignment) are manipulated or if partial views of faces are shown due toocclusion. This refers to a hotly-debated, yet highly-supported concept, knownas holistic face processing. We built a hierarchical computational model offace-processing based on evidence from recent neuronal and behavioural studieson faces processing in primates. Representational geometries of the last threelayers of the model have characteristics similar to those observed in monkeyface patches (posterior, middle and anterior patches). Furthermore, severalface-processing-related phenomena reported in the literature automaticallyemerge as properties of this model. The representations are evolved throughseveral computational layers, using biologically plausible learning rules. Themodel satisfies face inversion effect, composite face effect, other raceeffect, view and identity selectivity, and canonical face views. To ourknowledge, no models have so far been proposed with this performance andagreement with biological data.

A specialized face-processing network consistent with the representational geometry of monkey face patches