Integrating Globality and Locality for Robust Representation Based Classification

Zheng Zhang,Long Wang,Binglei Xie,Zhengming Li,Yan Chen

doi:10.1155/2014/415856

Abstract

The representation based classification method (RBCM) has shown huge potential for face recognition since it first emerged. Linear regression classification (LRC) method and collaborative representation classification (CRC) method are two well-known RBCMs. LRC and CRC exploit training samples of each class and all the training samples to represent the testing sample, respectively, and subsequently conduct classification on the basis of the representation residual. LRC method can be viewed as a “locality representation” method because it just uses the training samples of each class to represent the testing sample and it cannot embody the effectiveness of the “globality representation.” On the contrary, it seems that CRC method cannot own the benefit of locality of the general RBCM. Thus we propose to integrate CRC and LRC to perform more robust representation based classification. The experimental results on benchmark face databases substantially demonstrate that the proposed method achieves high classification accuracy.

Highlights

Face recognition has become a popular technique as one of the most promising branches of the pattern recognition [1,2,3,4]
In order to present the performance of the proposed method, several competitive face recognition methods are tested as comparison, such as collaborative representation classification (CRC) [30], Linear regression classification (LRC) [35], linear discriminant analysis (LDA), improved nearest neighbor classification (INNC) method [32], and the sparse representation based classification (SRC) algorithm proposed in [29]
CRC and SRC are the typical examples of global representation based classification method (RBCM) and LRC is one of the typical local RBCMs

Summary

Introduction

Face recognition has become a popular technique as one of the most promising branches of the pattern recognition [1,2,3,4]. The representation based classification method (RBCM) was proposed for face recognition by the specialists and scholars in the fields of computer vision and pattern recognition [5,6,7]. The RBCM are completely different from the conventional classification methods, such as principle component analysis (PCA) [8] and kernel PCA [9], linear discriminant analysis (LDA) [10], kernel LDA [11], and Gaussian maximum likelihood [12]. The RBCM generally uses the linear combination of the training samples to represent the test sample and utilizes the representation results to classify the test sample. The RBCM has been widely applied to the face recognition [13,14,15], image categorization [16, 17], and image superresolution [18, 19]

Methods

Results

Conclusion