Biologically inspired 3D face recognition from surface normals

A major consideration in state-of-the-art face recognition systems is the amount of data that is required to represent a face. Even a small (64×64) photograph of a face has 212 dimensions in which a face may sit. When large (>1 MB) photographs of faces are used, this represents a very large (and practically intractable) space and ways of reducing dimensionality without losing discriminatory information are needed for storing data for recognition. The eigenface technique, which is based upon Principal Components Analysis (PCA), is a well established dimension reduction method in face recognition research but does not have any biological basis. Humans excel at familiar face recognition and this paper attempts to show that modelling a biologically plausible process is a valid alternative approach to using eigenfaces for dimension reduction. Using a biologically inspired method to extract the certain facial discriminatory information which mirrors some of the idiosyncrasies of the human visual system, we show that recognition rates remain high despite 90% of the raw data being discarded