A Biologically Inspired Model for the Detection of External and Internal Head Motions

Non-verbal communication signals are to a large part conveyed by visual motion information of the user's facial components (intrinsic motion) and head (extrinsic motion). An observer perceives the visual flow as a superposition of both types of motions. However, when visual signals are used for training of classifiers for non-articulated communication signals, a decomposition is advantageous. We propose a biologically inspired model that builds upon the known functional organization of cortical motion processing at low and intermediate stages to decompose the composite motion signal. The approach extends previous models to incorporate mechanisms that represent motion gradients and direction sensitivity. The neural models operate on larger spatial scales to capture properties in flow patterns elicited by turning head movements. Center-surround mechanisms build contrast-sensitive cells and detect local facial motion. The model is probed with video samples and outputs occurrences and magnitudes of extrinsic and intrinsic motion patterns.