An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition

Jun Huang,Jamal El-Den,Kehua Su,Junlong Li,Tao Hu

doi:10.1155/2014/393265

Abstract

We proposed a face recognition algorithm based on both the multilinear principal component analysis (MPCA) and linear discriminant analysis (LDA). Compared with current traditional existing face recognition methods, our approach treats face images as multidimensional tensor in order to find the optimal tensor subspace for accomplishing dimension reduction. The LDA is used to project samples to a new discriminant feature space, while theKnearest neighbor (KNN) is adopted for sample set classification. The results of our study and the developed algorithm are validated with face databases ORL, FERET, and YALE and compared with PCA, MPCA, and PCA + LDA methods, which demonstrates an improvement in face recognition accuracy.

Highlights

Face recognition has become a topical and timely study focus in the fields of pattern recognition and computer vision for its wide application prospect [1, 2]
We proposed a face recognition algorithm based on both the multilinear principal component analysis (MPCA) and linear discriminant analysis (LDA)
After dimensionality reduction using MPCA, the matrices are arranged in columns into vectors as inputs to the LDA algorithm

Summary

Introduction

Face recognition has become a topical and timely study focus in the fields of pattern recognition and computer vision for its wide application prospect [1, 2]. In [12], Bansal and Chawla proposed normalized principal component analysis (NPCA) to improve the recognition rate They normalized images to remove the lightening variations by applying SVD instead of eigenvalue decomposition. The MPCA algorithm disregards the traditional method which is based on two-dimensional data and uses instead vectors and integrates multiple face images into a high-dimensional tensor and processes data in tensor space. The advantage of this approach lies in its ability to persistently structure facial information images and increases the accuracy rate when spatial relationships between pixels are considered.

Principle of MPCA

Process of the Recognition Algorithm

Experiments

Conclusions