Abstract
We propose a face recognition method that performs consistently in open space without constraints. The learning images are obtained not from an ideal world but from the real world, where users can move around freely. Automatically classification of many face images that vary according to the user?s position and posture is achieved through self-organization (unsupervised learning). A discrimination circuit is then created using only those face images that are suitable for recognition. The results show that the recognition rate for images with various facial angles in the real world can be improved by automatic classification through self-organization.
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