Feature Extraction for Face Recognition Based on Gabor Filters and Two-Dimensional Locality Preserving Projections

In this paper, two-dimensional locality preserving projections (2DLPP) was proposed to extract Gabor features for face recognition. 2DPCA is first utilized for dimensionality reduction of Gabor feature space, which is implemented directly from 2D image matrices. The objective of 2DLPP is to preserve the local structure of the image space by detecting the intrinsic manifold structure. In our method, an original image is convolved with Gabor filters corresponding to various orientations and scales to give its Gabor representation. 2DPCA is implemented in the row direction prior to 2DLPP in the column direction. Experiments are conducted on the ORL face database, which shows higher recognition performance of the proposed methods. The top recognition rate can reach 95.5%.