This paper presents motion field histograms as a new way of extracting
facial features and modeling expressions. Feature are based on local
receptive field histograms, which are robust against errors in
rotation, translation and scale changes during image alignment. Motion
information is also incorporated into the histograms by using
difference images instead of raw images. We take the principal
components of these histograms of selected facial regions and use the
top 20 eigenvectors for compact representation. The eigencoefficients
are then used to model the temporal structure of different facial
expressions from real-life data in the presence of translational and
rotational errors that arise from head-tracking. The results
demonstrate a 44\% average performance increase over traditional optic
flow method for expressions extracted from unconstrained interactions.