• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition
【24h】

Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition

机译：大规模人脸识别的两阶段非负稀疏表示

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 novel nonnegative sparse representation approach, called two-stage sparse representation (TSR), for robust face recognition on a large-scale database. Based on the divide and conquer strategy, TSR decomposes the procedure of robust face recognition into outlier detection stage and recognition stage. In the first stage, we propose a general multisubspace framework to learn a robust metric in which noise and outliers in image pixels are detected. Potential loss functions, including $L_{1}$ , $L_{2,1}$, and correntropy are studied. In the second stage, based on the learned metric and collaborative representation, we propose an efficient nonnegative sparse representation algorithm to find an approximation solution of sparse representation. According to the $L_{1}$ ball theory in sparse representation, the approximated solution is unique and can be optimized efficiently. Then a filtering strategy is developed to avoid the computation of the sparse representation on the whole large-scale dataset. Moreover, theoretical analysis also gives the necessary condition for nonnegative least squares technique to find a sparse solution. Extensive experiments on several public databases have demonstrated that the proposed TSR approach, in general, achieves better classification accuracy than the state-of-the-art sparse representation methods. More importantly, a significant reduction of computational costs is reached in comparison with sparse representation classifier; this enables the TSR to be more suitable for robust face recognition on a large-scale dataset.
机译：本文提出了一种新颖的非负稀疏表示方法，称为两阶段稀疏表示（TSR），用于在大型数据库上进行鲁棒的人脸识别。基于分而治之的策略，TSR将鲁棒的人脸识别过程分解为离群值检测阶段和识别阶段。在第一阶段，我们提出了一个通用的多子空间框架来学习一种鲁棒的度量，其中可以检测到图像像素中的噪声和离群值。潜在损失函数，包括 $ L_ {1} $ ， $ L_ {2,1} $ ，以及熵。在第二阶段，基于学习的度量和协同表示，我们提出了一种有效的非负稀疏表示算法，以找到稀疏表示的近似解。根据稀疏表示法中的 $ L_ {1} $ 球理论，近似解是唯一的，可以有效地进行优化。然后开发一种过滤策略，以避免在整个大型数据集上计算稀疏表示。此外，理论分析还为非负最小二乘技术找到稀疏解提供了必要条件。在几个公共数据库上进行的大量实验表明，与最先进的稀疏表示方法相比，建议的TSR方法通常可以实现更好的分类精度。更重要的是，与稀疏表示分类器相比，可显着降低计算成本；这使TSR更适合于大规模数据集上的鲁棒脸部识别。

著录项

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