...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neurocomputing >Sparse representation-based robust face recognition by graph regularized low-rank sparse representation recovery
【24h】

Sparse representation-based robust face recognition by graph regularized low-rank sparse representation recovery

机译：图正则化低秩稀疏表示恢复的基于稀疏表示的鲁棒人脸识别

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper proposes a graph regularized low-rank sparse representation recovery (GLRSRR) method for sparse representation-based robust face recognition, in which both the training and test samples might be corrupted because of illumination variations, pose changes, and occlusions. On the one hand, GLRSRR imposes both the lowest-rank and sparsest constraints on the representation matrix of the training samples, which makes the recovered clean training samples discriminative while maintaining the global structure of data. Simultaneously, GLRSRR explicitly encodes the local structure information of data and the discriminative information of different classes by incorporating a graph regularization term, which further improves the discriminative ability of the recovered clean training samples for sparse representation. As a result, a test sample is compactly represented by more clean training samples from the correct class. On the other hand, since the error matrix obtained by GLRSRR can accurately and intuitively characterize the corruption and occlusion of face image, it can be used as occlusion dictionary for sparse representation. This will bring more accurate representations of the corrupted test samples. The experimental results on several benchmark face image databases manifest the effectiveness and robustness of GLRSRR. (C) 2015 Elsevier B.V. All rights reserved.
机译：本文提出了一种基于稀疏表示的鲁棒人脸识别的图正则化低秩稀疏表示恢复（GLRSRR）方法，该方法中训练样本和测试样本都可能由于光照变化，姿势变化和遮挡而损坏。一方面，GLRSRR在训练样本的表示矩阵上同时施加了最低秩和最稀疏约束，这使得恢复的干净训练样本具有区别性，同时又保持了数据的整体结构。同时，GLRSRR通过结合图正则化项，对数据的局部结构信息和不同类别的判别信息进行显式编码，从而进一步提高了回收的干净训练样本对稀疏表示的判别能力。结果，测试样本由来自正确类别的更干净的训练样本紧凑地表示。另一方面，由于通过GLRSRR获得的误差矩阵可以准确，直观地表征人脸图像的破坏和遮挡，因此可以将其用作稀疏表示的遮挡字典。这将更准确地表示损坏的测试样本。在几个基准人脸图像数据库上的实验结果证明了GLRSRR的有效性和鲁棒性。 （C）2015 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Neurocomputing》 |2015年第21期|220-229|共10页
  • 作者

    Du Haishun; Zhang Xudong; Hu Qingpu; Hou Yandong;

  • 作者单位

    Henan Univ, Inst Image Proc & Pattern Recognit, Kaifeng 475004, Henan Province, Peoples R China;

    Henan Univ, Inst Image Proc & Pattern Recognit, Kaifeng 475004, Henan Province, Peoples R China;

    Henan Univ, Inst Image Proc & Pattern Recognit, Kaifeng 475004, Henan Province, Peoples R China;

    Henan Univ, Inst Image Proc & Pattern Recognit, Kaifeng 475004, Henan Province, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Sparse representation; Low-rank representation; Matrix recovery; Graph regularization; Face recognition;

    机译：稀疏表示;低秩表示;矩阵恢复;图正则化;人脸识别;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号