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基于全局约束的监督稀疏保持投影降维方法研究

Supervised Sparsity Preserving Projection Based on Global Constraint

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摘要

非约束环境下采集的人脸图像复杂多变,稀疏保持投影降维效果不理想。鉴于此,提出一种基于全局约束的监督稀疏保持投影(SSPP-GC)算法。通过引入监督超完备字典和类内紧凑度约束,增强同类非近邻样本的重构关系;并且,在低维投影时增加全局约束因子,使得投影矩阵既考虑了样本的局部稀疏关系,也考虑了全局分布特性,进一步消除异类伪近邻样本的低维映射影响。在AR库、Extended Yale B库、LFW库和PubFig库上进行实验仿真,大量实验结果验证了本文算法的有效性。

Abstract

The unconstrained face images collected in the real environments are influenced by many complicated and changeable interference factors, and sparsity preserving projection cannot well characterize the low-dimensional discriminant structure embedded in the high-dimensional unconstrained face images, which is important for the subsequent recognition task。 To solve this problem, we propose an effective dimensionality reduction method named as supervised sparsity preserving projections based on global constraint (SSPP-GC) which firstly enhances the reconstruction relationship of the same class of samples by adopting supervised over-complete dictionary and coefficient compactness constraints, and then appends the global constraint penalty in the step of the low-dimensional projection to further weaken the influence of other classes of samples。 The experimental results on AR, Extended Yale B, LFW and PubFig databases demonstrate the effectiveness of the proposed approach。

补充资料

中图分类号:TP181

DOI:

所属栏目:图像处理

基金项目:国家自然科学基金(61703201,KYTYJJG206)、江苏省自然科学基金(BK20170765)

收稿日期:2018-01-08

修改稿日期:2018-03-19

网络出版日期:2018-04-16

作者单位    点击查看

童莹:中国人民解放军陆军工程大学通信工程学院, 江苏 南京 210007南京工程学院通信工程学院, 江苏 南京 211167
魏以民:中国人民解放军陆军工程大学通信工程学院, 江苏 南京 210007
沈越泓:中国人民解放军陆军工程大学通信工程学院, 江苏 南京 210007

联系人作者:沈越泓(chunfeng22259@126.com)

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引用该论文

Tong Ying,Wei Yimin,Shen Yuehong. Supervised Sparsity Preserving Projection Based on Global Constraint[J]. Acta Optica Sinica, 2018, 38(9): 0910001

童莹,魏以民,沈越泓. 基于全局约束的监督稀疏保持投影降维方法研究[J]. 光学学报, 2018, 38(9): 0910001

被引情况

【1】程超,达飞鹏,王辰星,姜昌金. 基于Lucas-Kanade算法的最大Gabor相似度大姿态人脸识别. 光学学报, 2019, 39(7): 715005--1

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