Boosting Local Binary Pattern (LBP)-Based Face Recognition
Lecture notes in computer sciencePublished 1 January 2004
Guangcheng Zhang, Xiangsheng Huang, Stan Z. Li, Yangsheng Wang, Xihong Wu
Citations273
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
This paper presents a novel approach for face recognition by boosting statistical local features based classifiers using AdaBoost algorithm to learn a similarity of every face image pairs.
Abstract
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%.
Keywords
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