| 基于PCA和IGG权函数的人脸图像鲁棒线性回归分类方法 |
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| 引用本文： | 吕开云,鞠厦轶,龚循强,鲁铁定. 基于PCA和IGG权函数的人脸图像鲁棒线性回归分类方法[J]. 电子测量技术, 2021, 44(21): 152-157 |
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| 作者姓名： | 吕开云 鞠厦轶 龚循强 鲁铁定 |
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| 作者单位： | 1.东华理工大学 测绘工程学院 南昌 3300132.自然资源部 环鄱阳湖区域矿山环境监测与治理重点实验室 南昌 330013 |
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| 基金项目： | 国家自然科学基金项目（42101457;42061077）;江西省教育厅科学技术科技项目（GJJ150591）;东华理工大学放射性地质与勘探技术国防重点学科实验室开放基金（REGT1219）;2020年度江西省研究生创新专项资金项目立项项目（DHYC-202019）资助 |
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| 摘 要： | 在人脸识别中,线性回归分类是一种快速且有效的方法.然而,线性回归分类是基于图像向量进行识别,导致原始矩阵图像往往为高维数据,且人脸图像经常受到污染.为此,提出一种基于PCA和IGG权函数的鲁棒线性回归分类方法.首先通过PCA对人脸图像进行降维,再采用IGG权函数对被污染的人脸图像进行分类.选用公开的ORL和Yale人脸...
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| 关 键 词： | 人脸识别 主成分分析(PCA) IGG权函数 线性回归分类(LRC) |
Face recognition using robust linear regression classification based on PCA and IGG weight function |
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| Lv Kaiyun,Ju Xiayi,Gong Xunqiang,Lu Tieding. Face recognition using robust linear regression classification based on PCA and IGG weight function[J]. Electronic Measurement Technology, 2021, 44(21): 152-157 |
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| Authors: | Lv Kaiyun Ju Xiayi Gong Xunqiang Lu Tieding |
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| Affiliation: | 1. Faculty of Geomatics, East China University of Technology, Nanchang, 330013, China2 Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, Nanchang, 330013, China |
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| Abstract: | Linear regression classification is a fast and effective method in face recognition. However, linear regression classification is based on image vector recognition, which leads to the fact that the original matrix image is often high-dimensional data, and the face image is often contaminated. In order to solve this problem, a robust linear regression classification algorithm based on PCA and IGG weight function is proposed in this paper. Firstly, PCA is used to reduce the dimensionality of the face image, then the IGG weight function is adopted to classify the contaminated face image. Linear regression classification, robust linear regression classification based on IGG weight function and robust linear regression classification based on PCA and IGG weight function methods are compared with the public ORL and Yale databases. The experimental results show that the average recognition rate of the proposed method is above 92.07% without noise and with salt and pepper noise and speckle noise, which are higher than the other two methods in the ORL and Yale databases. |
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| Keywords: | face recognition principal component analysis (PCA) IGG weight function linear regression classification (LRC) |
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