基于低秩特征脸与协同表示的人脸识别算法

杨明中; 杨平先

doi:10.3788/YJYXS20173208.0650

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基于低秩特征脸与协同表示的人脸识别算法

图像处理 | 更新时间：2020-08-12

- 基于低秩特征脸与协同表示的人脸识别算法
- Face recognition algorithm based on low-rank eigenface and collaborative representation
- 液晶与显示 2017年32卷第8期页码：650-655
- 作者机构：
  
  四川理工学院自动化与信息工程学院, 四川自贡 643000
- 作者简介：
  
  [ "杨明中(1991-),男,四川南充人,硕士研究生,主研方向为图像处理、模式识别。E-mail:erdenzer@qq.com" ]
  [ "杨平先(1961-),男,四川自贡人,硕士,教授,主研方向为图像处理、信号处理。E-mail:ypingx@163.com" ]
- 基金信息：
- DOI：10.3788/YJYXS20173208.0650
  中图分类号： TP391
- 收稿日期：2017-01-17，
  
  录用日期：2017-5-18，
  
  纸质出版日期：2017-08-05
- 稿件说明：
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杨明中, 杨平先. 基于低秩特征脸与协同表示的人脸识别算法[J]. 液晶与显示, 2017,32(8):650-655.

Ming-zhong YANG, Ping-xian YANG. Face recognition algorithm based on low-rank eigenface and collaborative representation[J]. Chinese journal of liquid crystals and displays, 2017, 32(8): 650-655.
杨明中, 杨平先. 基于低秩特征脸与协同表示的人脸识别算法[J]. 液晶与显示, 2017,32(8):650-655. DOI： 10.3788/YJYXS20173208.0650.

Ming-zhong YANG, Ping-xian YANG. Face recognition algorithm based on low-rank eigenface and collaborative representation[J]. Chinese journal of liquid crystals and displays, 2017, 32(8): 650-655. DOI： 10.3788/YJYXS20173208.0650.

摘要

在人脸识别中，人脸图像往往受到表情、光照、遮挡、姿态变化的影响，对此本文提出一种基于低秩特征脸与协同表示的人脸识别算法。该算法先用低秩矩阵恢复算法分解出训练样本图像的误差图像，再分别对训练样本与误差图像提取特征构造特征字典，计算测试样本图像特征字典下的协同表示系数，最后通过重构误差进行分类。通过AR和ORL人脸库进行实验，结果表明，本文提出的人脸识别算法的识别率、识别速率得到有效提高。

Abstract

For face recognition

the face image tend to get variations of expression

lighting

occlusion and pose. In the paper

we propose a face recognition algorithm based on low-rank eigenface and collaborative representation. In the algorithm

we firstly obtain the error images of the training images using the low-rank matrix recovery algorithm. Then

we calculate the feature vectors of the training images and the corresponding error images to constitute a feature dictionary respectively

and calculate collaborative representation of test image with feature dictionary. Finally

the test image was classified by the reconstruction error. Experimental results on AR and ORL databases show that the proposed face image recognition algorithm has a higher recognition rate and fast.

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references

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