۱- Introduction

۲- Related works

۳- Robust coding based supervised sparse representation

۴- Sparsity and robustness of our model

۵- Experiment results

۶- Conclusions

References

Abstract

Sparse representation based classification (SRC) has become a popular methodology in face recognition in recent years. One widely used manner is to enforce minimum -norm on coding coefficient vector, which is considered as an unsupervised sparsity constraint and usually requires high computational cost. On the other hand, supervised sparsity representation based method (SSR) realizes sparse representation classification with higher efficiency by multiple phases of representing a probe. Nevertheless, since previous SSR methods only deal with Gaussian noise, they cannot satisfy empirical face recognition application which faces wide variations. In this paper, we propose a robust supervised sparse representation (RSSR) model, which uses two-phase of robust representation to compute a sparse coding vector. Huber loss is employed as the fidelity term in the linear representation, which improves the competitiveness of correct class in the first phase. Then training samples with weak competitiveness are removed by supervised way. In the second phase, the competitiveness of correct class is further boosted by Huber loss. We compare the RSSR with other state-of-the-art methods under different conditions, including illumination variations, gesture changes, expressions, corruptions, and occlusions. Comprehensive experiments on four open databases demonstrate the robustness of RSSR and competitive performance is obtained in dealing with face images with occlusion or not.

introduction

Using biometric identification technology for verifying identity is gaining more importance, and different techniques have been developed such as Palmprint recognition[1][2] and face recognition(FR)[3][4]. FR[5][6][7]has been extensively studied for its broad application prospects in recent years, such as authentication and payment system. The primary task of FR consists of feature extraction and classification[8][9][10]. For many classifiers, feature extraction that tends to discover discriminative feature is very important, which has great influence on recognition rate. Since there is rich redundancy in a face image, low dimensional features are extracted to concisely represent the samples in training set[11][12][13][14]. So that using these features can alleviate the computational cost and improve the recognition performance of classifiers. For empirical applications of FR, various changes including lighting, expression, pose, and occlusion can be seen in a probe and which could not included in training set, which leads to the consequence that the computed feature becomes inefficient. Image segmentation can be used to help the effective representation of an image by retaining the informative parts of images[15][16].