Multiple Data-Dependent Kernel Fisher Discriminant Analysis for Face Recognition

Yue Liu,Yibing Li,Hong Xie,Dandan Liu

doi:10.1155/2014/898560

Abstract

Kernel Fisher discriminant analysis (KFDA) method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variations of face images. However, a single kernel is not always suitable for the applications of face recognition which contain data from multiple, heterogeneous sources, such as face images under huge variations of pose, illumination, and facial expression. To improve the performance of KFDA in face recognition, a novel algorithm named multiple data-dependent kernel Fisher discriminant analysis (MDKFDA) is proposed in this paper. The constructed multiple data-dependent kernel (MDK) is a combination of several base kernels with a data-dependent kernel constraint on their weights. By solving the optimization equation based on Fisher criterion and maximizing the margin criterion, the parameter optimization of data-dependent kernel and multiple base kernels is achieved. Experimental results on the three face databases validate the effectiveness of the proposed algorithm.

Highlights

Face recognition has received extensive attention in many image processing applications
We conduct several experiments on three face databases to evaluate the performance of the proposed multiple data-dependent kernel Fisher discriminant analysis (MDKFDA) algorithm by comparing it with several widespread algorithms in face recognition, including principal component analysis (PCA), locality preserving projection (LPP), Fisher discriminant analysis (FDA), kernel-based PCA (KPCA), Kernel Fisher discriminant analysis (KFDA), and DKFDA
Three kernels are employed as the base kernels of multiple kernel function in multiple data-dependent kernel (MDK), including linear kernel, Gaussian kernel, and polynomial kernel

Summary

Introduction

Face recognition has received extensive attention in many image processing applications. By the utilization of learning-based approach, the original images can be mapped into a lower-dimensional feature space in which the essential structure of the original space becomes clear. To this end, Fisher discriminant analysis (FDA) [5], principal component analysis (PCA), and locality preserving projection (LPP) [6] are typical learning-based feature extraction techniques. Many nonlinear algorithms, such as kernel-based PCA (KPCA) [7] and FDA (KFDA) [8], have been devised and attained good performance in face recognition. To improve the performance of KFDA, we proposed a novel feature extraction algorithm for face recognition called multiple data-dependent kernel Fisher discriminant analysis (MDKFDA) based on the multiple kernel learning (MKL).

Related Work

Experimental Results and Discussions

Conclusions