This paper presents a novel Gabor-based kernel principal component analysis (PCA)
method by integrating the Gabor wavelet representation of face images and the kernel PCA …

In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly
nonlinear mapping is proposed for human face recognition. In our approach, the Gabor …

A kernel principal component analysis (PCA) was previously proposed as a nonlinear
extension of a PCA. The basic idea is to first map the input space into a feature space via …

The paper presents a novel method for the extraction of facial features based on the Gabor-
wavelet representation of face images and the kernel partial-least-squares discrimination …

We present an independent Gabor features (IGFs) method and its application to face
recognition. The novelty of the IGF method comes from 1) the derivation of independent …

This paper presents a novel pattern recognition framework by capitalizing on dimensionality
increasing techniques. In particular, the framework integrates Gabor image representation, a …

Principal component analysis (PCA) has been proven to be an efficient method in pattern
recognition and image analysis. Recently, PCA has been extensively employed for face …

A novel and uniform framework for both face identification and verification is presented in
this paper. The framework is based on a combination of Gabor wavelets and General …

Techniques that can introduce low-dimensional feature representation with enhanced
discriminatory power is of paramount importance in face recognition (FR) systems. It is well …

Factors such as misalignment, pose variation, and occlusion make robust face recognition a
difficult problem. It is known that statistical features such as local binary pattern are effective …