Face recognition using Fisher non-negative matrix factorization with sparseness constraints

被引：0
|
作者：
Pu, XR [1 ]
Yi, Z [1 ]
Zheng, ZM [1 ]
Zhou, W [1 ]
Ye, M [1 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Computat Intelligence Lab, Chengdu 610054, Sichuan, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 2, PROCEEDINGS | 2005年 / 3497卷
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：
A novel subspace method is proposed for part-based face recognition by using non-negative matrix factorization with sparseness constraints (NMFs) and Fisher's linear discriminant (FLD) hence its abbreviation, FNMFs. A comparative analysis engages PCA+FLD (FPCA) method and FNMFs method for both part-based and holistic-based face recognition. The comparative experiments axe completed for the ORL face database and UMIST face database, it shows that FNMFs has better performance than FPCA-based method both for holistic-face and parts-face images recognition.
引用
收藏
页码：112 / 117
页数：6
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