计算机科学 ›› 2018, Vol. 45 ›› Issue (4): 285-290.doi: 10.11896/j.issn.1002-137X.2018.04.048

• 图形图像与模式识别 • 上一篇    下一篇

基于噪声空间结构嵌入和高维梯度方向嵌入的鲁棒人脸识别方法

李小薪,李晶晶,贺霖,刘志勇   

  1. 浙江工业大学计算机科学与技术学院 杭州310023,浙江工业大学计算机科学与技术学院 杭州310023,华南理工大学自动化科学与工程学院 广州510641,深圳职业技术学院工业中心 广东 深圳518055
  • 出版日期:2018-04-15 发布日期:2018-05-11
  • 基金资助:
    本文受浙江省自然科学基金(LY18F020031),国家自然科学基金(61402411,61571195),广东省自然科学基金(2016A030313516),深圳市科技资助

Robust Face Recognition via Noise Spatial Structure Embedding and High Dimensional Gradient Orientation Embedding

LI Xiao-xin, LI Jing-jing, HE Lin and LIU Zhi-yong   

  • Online:2018-04-15 Published:2018-05-11

摘要: 基于核范数的矩阵回归方法(Nuclear norm based Matrix Regression,NMR)对人脸图像中因遮挡和光照变化等噪声引发的误差具有很强的鲁棒性。分析了NMR的鲁棒性的基本原理:首先,误差的核范数度量的是误差在其主方向上的能量,而主方向上的能量通常都去除了常规噪声的干扰;其次,误差的核范数度量嵌入了噪声的空间结构信息,而噪声的空间结构对于表示并排除噪声的影响至关重要。然而,仅仅考虑噪声的空间结构并不能有效消除噪声的影响。将具有噪声抑制能力的高维梯度方向(High-dimensional Gradient Orientation,HGO)特征嵌入NMR,提出了 一种基于高维梯度方向特征的NMR方法(High-dimensional Gradient Orientations-based NMR,HGO-NMR)极大地提升了NMR的识别性能。其重要意义在于指出噪声空间结构信息和噪声抑制机制对于面向现实的鲁棒人脸识别系统同等重要,单方面强调其中任何一种机制都将导致不稳定的识别性能。

关键词: 人脸识别,图像梯度方向,核范数,矩阵回归

Abstract: Nuclear norm based matrix regression(NMR) is robust to the errors caused by facial occlusion and illumination changes.This paper analyzed the underlying mechanism of NMR.Firstly,the nuclear norm measures the energies of the error caused by noises in the principle orientations,which usually exclude the influence of the common noises.Seco-ndly,the nuclear norm embeds the spatial structure of noises, which is very important to represent and exclude the noises.However,it is insufficient to eliminate the influence of the noises completely by only embedding the spatial structure of noises.As high-dimensional gradient orientation(HGO) has strong ability in noise cancellation, this paper embedded HGO into NMR and proposed a novel regression method called HGO-NMR.Experiments show that HGO-NMR outperforms NMR.The critical significance of HGO-NMR is that the noise spatial structure and the noise cancellation mechanism are equally important for face recognition system for reality,and only using one of the two mechanisms will lead to unstable recognition performance.

Key words: Face recognition,Image gradient orientation,Nuclear norm,Matrix regression

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