Supervised Filter Learning for Representation Based Face Recognition.

Chao Bi,Yugen Yi,Caixia Zheng,Lei Zhang,Jianzhong Wang,Miao Qi,Baoxue Zhang,Zhaohong Deng

doi:10.1371/journal.pone.0159084

Abstract

Representation based classification methods, such as Sparse Representation Classification (SRC) and Linear Regression Classification (LRC) have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances) in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP) features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm.

Highlights

Automatic face recognition has become a very active topic in computer vision and related research fields [1]
sparse representation based classification (SRC) first sparsely codes a query face image by the original training images, and the classification is performed by checking which class leads to the minimal representation residual of the query image
In order to overcome these limitations, we propose a new supervised filter learning (SFL) algorithm to improve the discriminative ability of Local Binary Pattern (LBP) features for representation based face recognition

Summary

Introduction

Automatic face recognition has become a very active topic in computer vision and related research fields [1]. Face recognition is still a very difficult task in practice due to the following two problematic factors. The performances of many recognition approaches degrade significantly in these cases. The representation based methods have been widely used in face recognition problem. In [2], Wright et al proposed a sparse representation based classification (SRC) method for face recognition. SRC first sparsely codes a query face image by the original training images, and the classification is performed by checking which class leads to the minimal representation residual of the query image. Naseem et al [3] proposed a linear regression based

Methods

Results

Conclusion