Based On Local Binary Pattern And Weber Local Descriptor Face Recognition

It is well known that face recognition is very popular in the research fields of pattern recognition and computer vision. Finding valid features to describe human faces is the key for face recognition. Generally, the current face recognition algorithms are classified into two categories, with one global-based and the other local-based.The Principal Component Analysis (PCA), the Kernel Principal Component Analysis (KPCA) and the 2D Principal Component Analysis (2DPCA) from the global-based category, have been extensively studied. PCA is a famous method for feature extraction. PCA is capable of linearly converting a high dimensional input vector to a lower dimensional one by calculating the eigenvector of the initially input covariance matrix. Various nonlinear PCA methods have also been developed till now KPCA is one of them, which introduces the kernel function into the algorithm. Compared with the conventional PCA, the 2DPCA extracts features based on the two dimensional image matrix rather than the one dimensional matrix.2DPCA builds the image covariance matrix directly through the image matrix, and then exports the eigenvector of the image matrix.The Local Binary Pattern (LBP) operator is one of the commonly used local-based face recognition method. With the properties of rotational invariance and gray invariance, the LBP operator has been developed constantly, and adopted in many areas, including texture classification, texture segmentation, and face recognition, to name a few. However, there is limitation of the LBP operator. This thesis aims at improving the performance of LBP. So far, I have done a lot to reach the aim.The main contributions of this paper are summarized as follows:(1) The background, fundamentals, and the application in face recognition of LBP were reviewed summarized in detail. At last, the method of LBP is proved effectual through the experiments in addition, I have examined the role of various parameters through all kind of experiments.(2) The texture recognition operator, WLD (the Webber Local Descriptor), was successfully applied in face recognition, and satisfactory results were achieved. The experiments conducted on ORL, Yale and AR human face database, showed that WLD is superior to most existing face recognition methods, such as PCA, KPCA,and2DPCA.(3) With the inspiration of WLD, LBP and WLD were combined to come up with a new operator, named WLBP, which improves the LBP operator. The experiments conducted on ORL, Yale and AR human face database, showed that WLBP is superior to most existing face recognition methods, such as PCA, KPCA,2DPCA,Gabor and LBP.