Discriminant sparse neighborhood preserving embedding for face recognition
Discriminant sparse neighborhood preserving embedding for face recognition
复制标题
用于人脸识别的判别稀疏邻域保留嵌入
DOI：
10.1016/j.patcog.2012.02.005
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发表时间：
2012-08-01
影响因子：
8
通讯作者：
Ji, Shuiwang
中科院分区：
文献类型：
--
作者：
Gui, Jie;Sun, Zhenan;Ji, Shuiwang
稀疏子空间学习近年来受到越来越多的关注。然而，大多数稀疏子空间学习方法是无监督的，不适合分类任务。在本文中，通过将判别信息添加到稀疏近邻保持嵌入（SNPE）中，提出了一种新的稀疏子空间学习算法，称为判别稀疏近邻保持嵌入（DSNPE）。DSNPE不仅保留了SNPE的稀疏重构关系，还从以下两个方面充分利用了全局判别结构：（1）将最大间隔准则（MMC）添加到DSNPE的目标函数中；（2）仅使用与当前样本具有相同标签的训练样本来计算稀疏重构关系。在三个人脸图像数据集（耶鲁大学数据集、扩展的耶鲁大学B数据集和AR数据集）上进行的大量实验证明了所提出的DSNPE方法的有效性。© 2012爱思唯尔有限公司。保留所有权利。
Sparse subspace learning has drawn more and more attentions recently. However, most of the sparse subspace learning methods are unsupervised and unsuitable for classification tasks. In this paper, a new sparse subspace learning algorithm called discriminant sparse neighborhood preserving embedding (DSNPE) is proposed by adding the discriminant information into sparse neighborhood preserving embedding (SNPE). DSNPE not only preserves the sparse reconstructive relationship of SNPE, but also sufficiently utilizes the global discriminant structures from the following two aspects: (1) maximum margin criterion (MMC) is added into the objective function of DSNPE; (2) only the training samples with the same label as the current sample are used to compute the sparse reconstructive relationship. Extensive experiments on three face image datasets (Yale, Extended Yale B and AR) demonstrate the effectiveness of the proposed DSNPE method. (C) 2012 Elsevier Ltd. All rights reserved.