Human skin is a complex surface, with fine scale geometry and local optical
properties that make its appearance difficult to model. Also, the conditions
under which the skin surface is viewed and illuminated greatly affect its
appearance. In this work, we capture the dependency of skin appearance on
imaging parameters using bidirectional imaging. We construct a new skin
texture database, containing bidirectional measurements of normal skin and
of skin affected by various disorders. The complete database contains more
than 3500 images, and is made publicly available for further research.
Furthermore, we present two computational models for use in skin texture
recognition. Both models are image-based representations of skin appearance
that account for the varied appearance of the skin with changes in
illumination and viewing direction. We employ these models in two contexts:
discrimination between different skin disorders (e.g., psoriasis vs. acne),
and classification of facial locations based on facial skin texture (e.g.,
forehead vs. chin). The classification experiments demonstrate the
usefulness of the modeling and measurement methods.
In Proceedings of Texture 2003 - The 3rd international workshop on texture analysis and synthesis, pp. 13-18, October 17th, 2003, Nice, France (co-located with International Conference on Computer Vision 2003).
This material is based upon work supported by the National Science Foundation under Grant No. 0092491 and Grant No. 0085864.