An application of fractal image-set coding in facial recognition

Faces are complex patterns that often differ in only subtle ways. Face recognition algorithms have difficulty in coping with differences in lighting, cameras, pose, expression, etc. We propose a novel approach for facial recognition based on a new feature extraction method called fractal image-set encoding. This feature extraction method is a specialized fractal image coding technique that makes fractal codes more suitable for object and face recognition. A fractal code of a gray-scale image can be divided in two parts – geometrical parameters and luminance parameters. We show that fractal codes for an image are not unique and that we can change the set of fractal parameters without significant change in the quality of the reconstructed image. Fractal image-set coding keeps geometrical parameters the same for all images in the database. Differences between images are captured in the non-geometrical or luminance parameters – which are faster to compute. Results on a subset of the XM2VTS database are presented.

Situating Design Research within Social Framework

This paper explores the expertise in industrial (product) design and contribution of knowledge generated trough the design research. Within this approach the research is situated within the social structure that constitutes people, activity, context and culture where an artifact is seen to be a mediator for the generation of new knowledge and its application. The paper concludes about the importance of research and practice integration and points out that situating the research around the artifacts, as mediators of knowledge, is transferable to Human-Computer Interaction field and any other area of the design research