Artificial noise elimination (ANE) has arisen as a promising strategy to counteract artificial
noise (AN) at the eavesdropper (Eve). However, conventional ANE schemes rely on the …

Abstract Regularized-LDA (R-LDA) is one of the most successful holistic approaches that is
introduced to overcome the “small sample size”(SSS) problem of the LDA method, which is …

In this paper, an illumination and expression robust feature face is generated to improve the
accuracy of facial recognition system. In the pre-processing stage, the median filter is …

Kernel Principal Component Analysis (KPCA) is the promotion of PCA in kernel space, Null
space LDA can be directly employed to choose a set of optimal projection vectors by …

Abstract Regularized-LDA (R-LDA) is a LDA-based method used for finding the nonzero
eigenvalues and the corresponding eigenvectors. This paper presents a RLDA-based …

Face recognition algorithms are mainly divided into Holistic and Feature based approaches.
Principal Component Analysis (PCA) and Fisher Discriminant Analysis (FDA) are the main …

Face Recognition is the process through which a person is identified by his facial image.
This technique makes it possible to use the facial images of a person to authenticate him …

There are a no. of face recognition algorithms including feature extraction, feature selection
and feature recognition. This is the area growing more & more implementing 3D pattern …