Impediments to Cosmopolitan Engagement: Technology and Late-Modern Cosmopolitanism

What characterises late modern variety of cosmopolitanism from its classical predecessors is the inherent connection between cosmopolitanism and technology. Technology enables a vital dimension of the cosmopolitan experience – to move beyond the cosmopolitan imagination to enable active, direct engagement with other cultures. Different types of technologies contribute to cosmopolitan practice but in this paper we focus on a specific set of these enabling technologies: technologies which play a crucial role in regulating the free movement of people and populations. We briefly examine how three of the great surveillance states of the 20th century – Nazi Germany, the Soviet Union, and the German Democratic Republic – used hightech solutions in pursuing an anti-cosmopolitanism. We suggest that in the period from 2001 to the present, important elements of the cosmopolitan ethos are being closed down, and once again high-tech is intimately connected to this moment. The increasing (and proposed) use of identity cards, biometric identification systems, ITS and GIS all work to make the globalised world much harder to traverse and inhibit the full expression and experience of cosmopolitanism. The result of these trends may be that the type of cosmopolitan sentiment exhibited in western countries is an ersatz, emptied out variety with little political-ethical robustness.

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.