COMPARISON OF PCA AND ICA IN FACE RECOGNITION

被引：2

作者：

Luo, Bing ^{[1
]}

Hao, Yu-Jie ^{[1
]}

Zhang, Wei-Hua ^{[2
]}

Liu, Zhi-Shen ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Chengdu 610054, Peoples R China

[2] Yankuang Cathay Coal Chem Co Ltd, Tengzhou, Peoples R China

来源：

2008 INTERNATIONAL CONFERENCE ON APPERCEIVING COMPUTING AND INTELLIGENCE ANALYSIS (ICACIA 2008) | 2008年

关键词：

Face recognition; PCA (principle component analysis); ICA (independent component analysis);

D O I：

10.1109/ICACIA.2008.4770014

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Over the last ten years, face recognition has become a specialized applications area within the larger field of computer vision. Principal component analysis (PCA) and independent component analysis (ICA) become common method for face recognition. This paper compares Principal component analysis (PCA) to independent component analysis (ICA) in face recognition. In this paper, we used PCA derived from "eigenfaces". ICA derived from a linear representation of nongaussian data. In the paper, it shows the different between PCA and ICA.

引用

页码：241 / +

页数：2

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