Face Recognition Using Neural Network Architecture

Abstract

Face recognition algorithms often have to solve problems such as facial pose,
illumination, and expression (PIE). To reduce the impacts, many researchers have been
trying to find the best discriminant transformation in eigenspaces, either linear or
nonlinear, to obtain better recognition results. Various researchers have also designed
novel matching algorithms to reduce the PIE effects. In this study, a nearest feature space
embedding (called NFS embedding) algorithm is proposed for face recognition. The
distance between a point and the nearest feature line (NFL) or the NFS is embedded in the
transformation through the discriminant analysis. Three factors, including class
reparability, neighborhood structure preservation, and NFS measurement, were
considered to find the most effective and discriminating transformation in eigenspaces.
The proposed method was evaluated by several benchmark databases and compared with
several state-of-the-art algorithms. According to the compared results, the proposed
method outperformed the other algorithms