Boosting local binary pattern (LBP)-based face recognition

被引：0
|
作者：
Zhang, GC [1 ]
Huang, XS
Li, SZ
Wang, YS
Wu, XH
机构：
[1] Peking Univ, Ctr Informat Sci, Beijing 100871, Peoples R China
[2] Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China
来源：
ADVANCES IN BIOMETRIC PERSON AUTHENTICATION, PROCEEDINGS | 2004年 / 3338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：
This paper presents a novel approach for face recognition by boosting statistical local features based classifiers. The face image is scanned with a scalable sub-window from which the Local Binary Pattern (LBP) histograms [14] are obtained to describe the local features of a face image. The multi-class problem of face recognition is transformed into a two-class one by classifying every two face images as intra-personal or extra-personal ones [9]. The Chi square distance between corresponding Local Binary Pattern histograms of two face images is used as discriminative feature for intra/extra-personal classification. We use AdaBoost algorithm to learn a similarity of every face image pairs. The proposed method was tested on the FERET FA/FB image sets and yielded an exciting recognition rate of 97.9%.
引用
收藏
页码：179 / 186
页数：8
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