Abstract

A framework for recognising human faces from unfamiliar views is described and a simple implementation of this framework evaluated. The interaction between training view and testing view is shown to compare with observations in human face recognition experiments. The ability of the system to learn from several training views, as available in video footage, is shown to improve the overall performance of the system as is the use of multiple testing images. 1 Introduction Recognising faces from previously unseen viewpoints is inherently more difficult than matching faces at the same view. Simple image comparisons such as correlation demonstrate that there is a greater difference between different viewpoints of the same subject than between different subjects at the same view which means that the recognition method used must take into account the non-linear variations of faces with viewpoint. In order to achieve recognition of previously unseen views, we require a method of relating the...

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