Abstract
The surface curvatures extracted from the face contain the most important personal facial information. In this paper, we developed a range face recognition method that uses back-propagation(BP) that optimizes initial parameters such as bias values or weights, which is combined by face component including the curvature attributes. In the first step, the proposed approach calculates face curvatures which present the facial features for the normalized range face image using the singular value decomposition (SVD). In the second step, PCA and Fisherface method are applied to each component range face to generate the reduced image
dimension. In the last step, the back-propagation's weight
is trained using the produced low-dimensional vectors and
individual classifiers. The experimental results show that
the proposed approach has outstanding classification in
comparing with other methods.
