Robust Manifold Embedding for Face Recognition

Zhonghua Liu,Qingtao Wu,Kaiming Shi,Kaibing Zhang,Lingyun Xiang

doi:10.1109/access.2020.2997953

Abstract

Flexible manifold embedding (FME) has been recognized as an effective method for face recognition by integrating both class label information from labeled data and manifold structure information of all data. In order to achieve better performance, this particular method usually requires sufficient samples to make manifold smooth. However, it is often hard to provide enough samples for FME in practice. In view of facial symmetry, we utilize left/right mirror face images to address the deficiency of samples in manifold embedding. These mirror images enable to reflect variations of illuminations, or poses or both them that the original face images cannot provide. Therefore, we propose a robust manifold embedding (RME) algorithm in this paper, which can fully use the class label information and correctly capture the underlying manifold structure. The proposed RME algorithm integrates two complementary characteristics of the label fitness and the manifold smoothness. Moreover, the original face images and its left/right mirror images are jointly used in the learning of RME, which shows better robustness against the variations of both illuminations and poses. Extensive experiments on several public face databases demonstrate that the proposed RME algorithm is promising for higher recognition accuracy than other compared methods in reference.

Highlights

Dimension reduction is a hot topic for the recognition tasks of high dimensional image
Inspired by the prior work proposed in [43] and the symmetry feature of human faces [44], we propose a novel robust manifold embedding (RME) algorithm
EXPERIMENTAL RESULTS we carry out extensive experiments to evaluate the performance of the proposed RME algorithm by comparing with other five state-of-art algorithms: MSEC [52], collaborative representation based classification (CRC) [35], flexible manifold embedding (FME) [39], SOSI [24], manifold discriminant regression learning (MDRL) [25], the method presented in [42], GFHF [43], and NLDLSR [53]

Summary

INTRODUCTION

Dimension reduction is a hot topic for the recognition tasks of high dimensional image. These methods cannot obtain a projection matrix to map a new sample to the desired low-dimensional embedding space To solve this issue, many researchers focused on many improved manifold learning methods. In order to utilize the local structure information of the data, Lu et al [25] proposed a novel manifold linear regression framework. It is well known that each feature of the observed data has different contribution to the pattern representation and classification Based on this fact, Yang et al [36] proposed a relaxed collaborative representation (RCR) method. In order to effectively use label information and manifold structure of the observed data, Nie et al [39] presented a unified manifold learning framework called flexible manifold embedding (FME) by employing a linear regression function to map a new sample to desired feature space.

BRIEF REVIEW OF RELATED WORKS

EXPERIMENTAL RESULTS

PARAMETERS SELECTION

CONCLUSION