Face Identification and Verification Using PCA and LDA

被引：0
|
作者：
Chan, Lih-Heng [1 ]
Salleh, Sh-Hussain [1 ]
Ting, Chee-Ming [1 ]
Ariff, A. K. [1 ]
机构：
[1] Univ Teknol Malaysia, Fac Biomed Engn & Hlth Sci, Ctr Biomed Engn, Skudai 81300, Johor, Malaysia
来源：
INTERNATIONAL SYMPOSIUM OF INFORMATION TECHNOLOGY 2008, VOLS 1-4, PROCEEDINGS: COGNITIVE INFORMATICS: BRIDGING NATURAL AND ARTIFICIAL KNOWLEDGE | 2008年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Algorithms based on PCA (Principal Components Analysis) and LDA (Linear Discriminant Analysis) are among the most popular appearance-based approaches in face recognition. PCA is recognized as an optimal method to perform dimension reduction, yet being claimed as lacking discrimination ability. LDA once proposed to obtain better classification by using class information. Disputes over the comparison of PCA and LDA have motivated us to study their performance. In this paper, we describe both of these statistical subspace methods and evaluated them using The Database of Faces which comprises 40 subjects with 10 images each. Both identification and verfication results have revealed the superiority of LDA over PCA for this medium-sized database.
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收藏
页码：1310 / 1315
页数：6
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