Local uncorrelated local discriminant embedding for face recognition

Xiao-hu MA; Meng YANG; Zhao ZHANG

doi:10.1631/FITEE.1500255

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Local uncorrelated local discriminant embedding for face recognition

Special Feature on Visual and Crowd Computing | Updated：2022-05-19

- Local uncorrelated local discriminant embedding for face recognition
- 局部不相关的局部判别嵌入人脸识别算法
- Frontiers of Information Technology & Electronic Engineering Vol. 3, Issue 3, Pages: 212-223(2016)
- Affiliations：
  
  1School of Computer Science and Technology, Soochow University, Suzhou 215006, China
  2State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China
- Author bio：
  
  E-mail: xhma@suda.edu.cn
  E-mail: eyangmeng@163.com
  E-mail: cszzhang@suda.edu.cn
- Funds：
- DOI：10.1631/FITEE.1500255
  CLC： TP391.4
- Received：07 August 2015，
  
  Accepted：02 December 2015，
  
  Published：2016-03
- Accepted：
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Xiao-hu MA, Meng YANG, Zhao ZHANG. Local uncorrelated local discriminant embedding for face recognition[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 3(3): 212-223.
DOI：

Xiao-hu MA, Meng YANG, Zhao ZHANG. Local uncorrelated local discriminant embedding for face recognition[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 3(3): 212-223. DOI： 10.1631/FITEE.1500255.

摘要

目的

统计不相关是一种重要的性质，然而一些人脸识别算法常将这一性质忽略。统计不相关准则目的是使得特征线性不相关，消除提取的判别特征之间的冗余信息。已有的一些算法只是分别考虑数据集的全局统计不相关特征和数据集的局部的不相关特性。为解决这一问题，本文提出一种新的特征提取算法—局部不相关的局部判别嵌入算法（local uncorrelated local discriminant embedding，LULDE），该算法能同时考虑数据集中的同类和异类样本点的局部信息。

创新点

该算法有三点贡献：（1）提出了一种新的局部不相关准则，能同时利用数据集中的同类和异类样本点的局部信息；（2）重新构造局部判别嵌入算法中的本征图

和惩罚图

对应的邻接矩阵，使得算法比原有的局部判别嵌入算法具有更强的判别能力；（3）利用一种不同于PCA预处理的方式解决了“小样本”问题。

方法

首先，重新定义本征图的邻接矩阵

和惩罚图的邻接矩阵

。然后确定LULDE算法的目标函数。最后，通过求解特征值问题得到最优投影矩阵。

结论

在Yale，ORL，Extended Yale B和FERET四个常用人脸数据库上的大量实验结果表明了本算法的有效性。

Abstract

The feature extraction algorithm plays an important role in face recognition. However

the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is that it eliminates the redundancy among the extracted discriminant features

while many algorithms generally ignore this property. In this paper

we introduce a novel feature extraction method called local uncorrelated local discriminant embedding (LULDE). The proposed approach can be seen as an extension of a local discriminant embedding (LDE) framework in three ways. First

a new local statistical uncorrelated criterion is proposed

which effectively captures the local information of interclass and intraclass. Second

we reconstruct the affinity matrices of an intrinsic graph and a penalty graph

which are mentioned in LDE to enhance the discriminant property. Finally

it overcomes the small-sample-size problem without using principal component analysis to preprocess the original data

which avoids losing some discriminant information. Experimental results on Yale

ORL

Extended Yale B

and FERET databases demonstrate that LULDE outperforms LDE and other representative uncorrelated feature extraction methods.

关键词

Keywords

references

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