Face recognition based on sub-pattern two-directional two-dimensional linear discriminant analysis

DONG Xiao-qing; CHEN Hong-cai

doi:10.3788/YJYXS20153006.1016

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Face recognition based on sub-pattern two-directional two-dimensional linear discriminant analysis

Imaging Technology & Image Processing | 更新时间：2020-08-12

- Face recognition based on sub-pattern two-directional two-dimensional linear discriminant analysis
- Chinese Journal of Liquid Crystals and Displays Vol. 30, Issue 6, Pages: 1016-1023(2015)
- 作者机构：
  
  韩山师范学院物理与电子工程学院, 广东潮州 521041
- 作者简介：
- 基金信息：
  
  Supported by Project of Education Department of Guangdong(No.2014EQNCX194);2014 Te
- DOI：10.3788/YJYXS20153006.1016
  CLC： TP391
- Received：23 September 2014，
  
  Revised：29 October 2014，
  
  Published：05 December 2015
- 稿件说明：
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董晓庆, 陈洪财,. 基于子模式双向二维线性判别分析的人脸识别[J]. 液晶与显示, 2015,30(6): 1016-1023

DONG Xiao-qing, CHEN Hong-cai,. Face recognition based on sub-pattern two-directional two-dimensional linear discriminant analysis[J]. , 2015,30(6): 1016-1023
董晓庆, 陈洪财,. 基于子模式双向二维线性判别分析的人脸识别[J]. 液晶与显示, 2015,30(6): 1016-1023 DOI： 10.3788/YJYXS20153006.1016.

DONG Xiao-qing, CHEN Hong-cai,. Face recognition based on sub-pattern two-directional two-dimensional linear discriminant analysis[J]. , 2015,30(6): 1016-1023 DOI： 10.3788/YJYXS20153006.1016.

摘要

针对表情和光照变化等对人脸识别影响的问题

提出一种基于子模式双向二维线性判别分析(Sub-pattern two-directional two-dimensional linear discriminant analysis

Sp-(2D)

LDA)的人脸识别方法。该方法首先对原图像进行分块处理

并保持子块间的空间关系

然后对各个子训练样本集从行方向和列方向同时利用2DLDA进行特征抽取

最后把各个子特征矩阵拼接成一对应原始图像的特征矩阵

并采用最近邻分类器进行分类识别。在ORL及Yale人脸库上的试验结果表明

Sp-(2D)

LDA有效降低了鉴别特征的维数

减少了表情和光照变化的影响

获得了较好的识别性能。

Abstract

To reduce the impacts of the variations of expression and illumination

a novel face recognition method based on sub-pattern two-directional two-dimensional linear discriminant analysis(Sp-(2D)

LDA) is presented in this paper. Firstly

Sp-(2D)

LDA divides the original images into smaller sub-images and keeps the spatial relationship between the sub-images. Secondly

it simultaneously applies 2DLDA to the subsets of the training samples in the row and column directions to extract local sub-features. Finally

the sub-features are synthesized into global features and nearest neighbor classifier is used for classification. The experimental results on Yale and ORL face databases show that the proposed Sp-(2D)

LDA method effectively reduce not only the dimension of the eigenvectors

but also the influence of variations in illumination and facial expression. Thus

the proposed method has better classification performances than the other related methods.

关键词

Keywords

references

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