Face recognition based two dimensional locality preserving projection in frequency domain
MetadataShow full item record
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.
Showing items related by title, author, creator and subject.
Lu, C.; Liu, X.; Liu, Wan-quan (2010)In this paper we investigate the face recognition problem via using the two dimensional locality preserving projection in frequency domain. For this purpose, we first introduce the two-dimensional locality preserving ...
Alrjebi, M.; Liu, Wan-Quan; Li, Ling (2018)In this paper, a new approach named as the Two Directional Multi-level Threshold-LBP Fusion (2D–MTLBP-F) is proposed to solve the problem of face recognition against illuminations. The proposed approach utilizes the ...
Wang, H.; Song, W.; Liu, Wan-Quan; Song, N.; Wang, Y.; Pan, H. (2018)Face recognition/verification has received great attention in both theory and application for the past two decades. Deep learning has been considered as a very powerful tool for improving the performance of face ...