Local descriptors and tensor local preserving projection in face recognition

Abstract

In this paper, a new multi-dimensional facial recognition system is proposed. A new technique for data reduction for multidimensional biometric facial analysis to improve face recognition performance in real environments is implemented. For this the tensorial methods are adopted, the sample of the face must be reshaped by natural tensor representations into vectors of very large dimensions. This remodeling breaks the natural structure of the correlations existing in the original tensor data, involving high costs and the need to evaluate a large number of parameters. Firstly, we give an overview and generalities on facial recognition systems, and then we present some techniques to n Dimensional Face Recognition System (nDFRS). The Tensor Local Preserving Projection (TLPP) is proposed as a new method of reducing and implemented to obtain our Nearest Neighbor classification. TLPP is used to reduce features vectors obtained by local descriptors LBP, LPQ and BSI. Many experiments on ORL, YALE and FERET Databases show that our methods are not only more effective but also more robust.