Recognizing human facial expressions in a potential field

The field of induction on the retina, called potential field, which is similar to the human recognition mechanism, plays a very important role in perceiving human faces. This paper presents a new idea for recognizing human facial expressions from an overall pattern of the face, represented in a potential field activated by edges in a single input image. A 2D grid, called potential net, of which nodes are moved by the image force of the edges and springs connected to their four neighbors is used as a model of the potential field. Since the dimension of the space obtained by the potential net is too high, it is mapped into a low dimensional space, called emotion space, by applying the K-L expansion. Unknown expressions in input images are estimated from their mapping into the emotion space.