Face recognition based on subset selection via metric learning on manifold

SHAO Hong; CHEN Shuang; ZHAO Jie-yi; CUI Wen-cheng; YU Tian-shu

doi:10.1631/FITEE.1500085

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Face recognition based on subset selection via metric learning on manifold

Computer & Automation | Updated：2022-06-06

- Face recognition based on subset selection via metric learning on manifold
- 基于度量学习和子集选择的稀疏表示人脸识别算法
- Frontiers of Information Technology & Electronic Engineering Vol. 16, Issue 12, Pages: 1046-1058(2015)
- Affiliations：
  
  1.School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China
  2.The University of Texas Health Science Center at Houston, Houston 77030, USA
  3.Schulich School of Engineering, University of Calgary, Calgary T2N 1N4, Canada
- Author bio：
  
  Shuang CHEN, E-mail: chenshuang19891129@gmail.com
- Funds：
- DOI：10.1631/FITEE.1500085
  CLC： TP391
- Received：19 March 2015，
  
  Revised：2015-11-11，
  
  Published：2015-12
- Accepted：
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SHAO Hong, CHEN Shuang, ZHAO Jie-yi, et al. Face recognition based on subset selection via metric learning on manifold[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(12): 1046-1058.
DOI：

SHAO Hong, CHEN Shuang, ZHAO Jie-yi, et al. Face recognition based on subset selection via metric learning on manifold[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(12): 1046-1058. DOI： 10.1631/FITEE.1500085.

摘要

目的

针对传统基于稀疏表示分类的人脸识别算法计算复杂度高的劣势，通过分析人脸图像的流形结构，提出一种通过度量学习和子集选择的人脸识别算法，极大地加快基于稀疏表示的人脸识别速度，在不牺牲算法识别性能的前提下，使其可以达到实用的计算速度要求。

创新点

对

-人脸识别算法提出了一种更精确的字典选择框架，该框架主要包含一种考虑类内类间差的人脸流形结构和可沿流形结构进行子集选择的fast marching算法。

方法

在训练阶段，首先将度量学习引入到人脸识别问题中，利用度量学习产生一个线性投影矩阵，所有训练图像经该矩阵投影后能保持最小类内差与最大类间差。其次，结合人脸图像的流形结构进行建模，利用上述步骤学习得到的度量将所有训练图像构成一个无向带权值的连通邻接图，该图可充分表达训练样本在流形中的几何结构。在识别阶段，fast marching以被查询图像为基点在训练阶段得到的人脸流形结构上搜索子集，最终稀疏表示算法以子集为字典识别出被查询人脸图像的类别。

结论

使用通用数据库验证算法的识别准确率和计算效率。实验表明，文章所提出的算法能够有效的利用训练样本的类内和类间信息，达到可观的识别性能。该方法同时将识别速度提高到了可以实用的程度。

Abstract

With the development of face recognition using sparse representation based classification (SRC)

many relevant methods have been proposed and investigated. However

when the dictionary is large and the representation is sparse

only a small proportion of the elements contributes to the $l^1$-minimization. Under this observation

several approaches have been developed to carry out an efficient element selection procedure before SRC. In this paper

we employ a metric learning approach which helps find the active elements correctly by taking into account the interclass/intraclass relationship and manifold structure of face images. After the metric has been learned

a neighborhood graph is constructed in the projected space. A fast marching algorithm is used to rapidly select the subset from the graph

and SRC is implemented for classification. Experimental results show that our method achieves promising performance and significant efficiency enhancement.

关键词

Keywords

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