Paper Info
Title
Gender classification based on boosting local binary pattern
Abstract
This paper presents a novel approach for gender classification by boosting local binary pattern-based classifiers. The face area is scanned with scalable small windows from which Local Binary Pattern (LBP) histograms are obtained to effectively express the local feature of a face image. The Chi square distance between corresponding Local Binary Pattern histograms of sample image and template is used to construct weak classifiers pool. Adaboost algorithm is applied to build the final strong classifiers by selecting and combining the most useful weak classifiers. In addition, two experiments are made for classifying gender based on local binary pattern. The male and female images set are collected from FERET databases. In the first experiment, the features are extracted by LBP histograms from fixed sub windows. The second experiment is tested on our boosting LBP based method. Finally, the results of two experiments show that the features extracted by LBP operator are discriminative for gender classification and our proposed approach achieves better performance of classification than several others methods.
Year
DOI
Venue
2006
10.1007/11760023_29
ISNN (2)
Keywords
Field
DocType
face area,gender classification,classifying gender,local binary pattern,lbp operator,local binary pattern-based classifier,weak classifiers pool,lbp histogram,corresponding local binary pattern,local feature,feature extraction
Facial recognition system,Histogram,Pattern recognition,Computer science,Local binary patterns,Image processing,Supervised learning,Artificial intelligence,Boosting (machine learning),Artificial neural network,Discriminative model,Machine learning
Conference
Volume
ISSN
ISBN
3972
0302-9743
3-540-34437-3
Citations 
PageRank 
References 
44
1.68
8
Authors
5
Name
Order
Citations
PageRank
Ning Sun111813.20
Wenming Zheng2124080.70
Changyin Sun32002157.17
Cairong Zou441527.19
Li Zhao538027.36