  We present a method for extracting information about facial expressions from
digital images. The method codes facial expression images using a
multi-orientation, multi-resolution set of Gabor filters that are
topographically ordered and approximately aligned with the face. A similarity
space derived from this code is compared with one derived from semantic ratings
of the images by human observers. Interestingly the low-dimensional structure
of the image-derived similarity space shares organizational features with the
circumplex model of affect, suggesting a bridge between categorical and
dimensional representations of facial expression. Our results also indicate
that it would be possible to construct a facial expression classifier based on
a topographically-linked multi-orientation, multi-resolution Gabor coding of
the facial images at the input stage. The significant degree of psychological
plausibility exhibited by the proposed code may also be useful in the design of
human-computer interfaces.
