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ICANN
2009
Springer

Learning from Examples to Generalize over Pose and Illumination

12 years 4 months ago
Learning from Examples to Generalize over Pose and Illumination
We present a neural system that recognizes faces under strong variations in pose and illumination. The generalization is learnt completely on the basis of examples of a subset of persons (the model database) in frontal and rotated view and under different illuminations. Similarities in identical pose/illumination are calculated by bunch graph matching, identity is coded by similarity rank lists. A neural network based on spike timing decodes these rank lists. We show that identity decisions can be made on the basis of few spikes. Recognition results on a large database of Chinese faces show that the transformations were successfully learnt.
Marco K. Müller, Rolf P. Würtz
Added 25 Jul 2010
Updated 25 Jul 2010
Type Conference
Year 2009
Where ICANN
Authors Marco K. Müller, Rolf P. Würtz
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