Abstract
In this paper, we present a multiple scale neural architecture for face recognition. The architecture is composed of several stages: face detection, Difference of Gaussians, Gabor filter bank, Principal Component Analysis, and two-stage MLPs. The architecture was evaluated using two well-known face databases. A detailed study of all the parameters that influence the architecture performance was carried out. The architecture achieved a correct detection rate of 84% with face images changing in pose and gesture.
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