Low-Frequency Representation for Face Recognition

被引：0
|
作者：
Wang, Bangjun [1 ,2 ,3 ]
Zhang, Li [2 ,3 ]
Li, Fanzhang [1 ,2 ,3 ]
机构：
[1] Beijing Jiaotong Univ, Sch Comp & Informat Technol, Machine Learning & Cognit Comp Lab, Beijing 100044, Peoples R China
[2] Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China
[3] Soochow Univ, Joint Int Res Lab Machine Learning & Neuromorph C, Suzhou 215006, Peoples R China
来源：
NEURAL INFORMATION PROCESSING (ICONIP 2017), PT VI | 2017年 / 10639卷
基金：
中国国家自然科学基金;
关键词：
Face recognition; Support value transform; Low-frequency representation; Feature extraction; Image representation; DISCRIMINANT-ANALYSIS;
D O I：
10.1007/978-3-319-70136-3_54
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes a low-frequency representation (LFR) method for face images based on support value transform. LFR works directly on 2D image matrices rather than 1D vectors, thus the image matrix does not need to be transformed into a vector prior to feature extraction. In LFR, the simple and slowly variational features for face images are remained. To demonstrate the effectiveness of LFR, a series of experiments are performed on two face image databases: ORL and UMIST face databases. Experimental results indicate that LFR provides a better representation for face images with multi-view and slightly various illumination.
引用
收藏
页码：510 / 519
页数：10
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