Face recognition based on two dimensional locality preserving projections in frequency domain
In this paper we propose a new face recognition method based on two-dimensional locality preserving projections (2DLPP) in frequency domain. For this purpose, we first introduce the two-dimensional locality preserving projections. Then the 2DLPP in frequency domain is proposed for face recognition. In fact, two dimensional discrete cosine transform (2DDCT) is used as a pre-processing step and it transforms the face image signals from spatial domain into frequency domain aiming to reduce the effects of illumination and pose changes in face recognition. Then 2DLPP is applied on the upper left corner blocks of the 2DDCT transformed matrices, which represent main energy of each original image. For demonstration, the Olivetti Research Laboratory (ORL), YALE, FERET and YALE-B face datasets are used to compare the proposed approach with the conventional 2DLPP and 2DDCT approaches with the nearest neighborhood (NN) classifier. The experimental results show that the proposed 2DLPP in frequency domain is superior over the 2DLPP in spatial domain and 2DDCT itself in frequency domain.
face recognition two dimensional locality preserving projections two dimensional discrete cosine transform