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

Coding Facial Expressions with Gabor Wavelets (IVC Special Issue)