13 Elements for a Neural Theory of the Processing of Dynamic Faces

Face recognition has been a central topic in computer vision for at least two decades and progress in recent years has been significant. Automated face recognition systems are now widespread in applications ranging from surveillance to personal computers. In contrast, only a handful of neurobiologically plausible computational models have been proposed to try to explain the processing of faces in the primate cortex (e.g., Giese & Leopold, 2005; Jiang et al., 2006), and no such model has been applied specifically in the context of dynamic faces. There is a need for an integrated computational theory of dynamic face processing that could integrate and summarize evidence obtained with di¤erent experimental methods, from single-cell physiology to fMRI, MEG, and ultimately behavior and psychophysics. At the same time, physiologically plausible models capable of processing real video sequences constitute a plausibility proof for the computational feasibility of di¤erent hypothetical neural mechanisms. In this review chapter we will first discuss computer vision models for the processing of dynamic faces; these do not necessarily try to reproduce biological data but may suggest relevant computational principles. We then provide an overview of computational neuroscience models for the processing of static faces and dynamic body stimuli. We further highlight specific elements from our own work that are likely to be relevant for the processing of dynamic face stimuli. The last section discusses open problems and critical experiments from the viewpoint of neural computational approaches to the processing of dynamic faces.