Color Two-Dimensional Principal Component Analysis for Face Recognition Based on Quaternion Model

被引：22
|
作者：
Jia, Zhi-Gang [1 ]
Ling, Si-Tao [2 ]
Zhao, Mei-Xiang [1 ,3 ]
机构：
[1] Jiangsu Normal Univ, Sch Math & Stat, Jiangsu Key Lab Educ Big Data Sci & Engn, Xuzhou 221116, Jiangsu, Peoples R China
[2] China Univ Min & Technol, Sch Math, Xuzhou 221116, Jiangsu, Peoples R China
[3] China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 221116, Jiangsu, Peoples R China
来源：
INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT I | 2017年 / 10361卷
基金：
中国国家自然科学基金;
关键词：
Color face recognition; Eigenface; Quaternion matrix; Color; 2DPCA; PCA; EIGENFACES;
D O I：
10.1007/978-3-319-63309-1_17
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The color two-dimensional principal component analysis (color 2DPCA) approach based on quaternion model is presented for color face recognition. Based on 2D quaternion matrices rather than 1D quaternion vectors, color 2DPCA combines the color information and the spatial characteristic for face recognition, and straightly computes the low-dimensional covariance matrix of the training color face images and determines the corresponding eigenvectors in a short CPU time. The image reconstruction theory is also built on color 2DPCA. The experiments on real face data sets are provided to validate the feasibility and effectiveness of the proposed algorithm.
引用
收藏
页码：177 / 189
页数：13
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