文章基本信息

标题：Gender Perception From Faces Using Boosted LBPH (Local Binary Patten Histograms)
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作者：U. U. Tariq ; W. Ahmad ; M. D. Abdullah Asif 等
期刊名称：Carptahian Journal of Electronic and Computer Engineering
印刷版ISSN：1844-9689
电子版ISSN：2343-8908
出版年度：2013
卷号：6
期号：1
页码：8
语种：English
出版社：UT Press Publishing House, Technical University of ClujNapoca, Romania
摘要：Automatic Gender classification from faces has several applications such as surveillance, human computer interaction, targeted advertisement etc. Humans can recognize gender from faces quite accurately but for computer vision it is a difficult task. Many studies have targeted this problem but most of these studies used images of faces taken under constrained conditions. Real-world applications however require to process images from real-world, that have significant variation in lighting and pose, which makes the gender classification task very difficult. We have examined the problem of automatic gender classification from faces on real-world images. Using a face detector faces from images are extracted aligned and represented using Local binary pattern histogram. Discriminative features are selected using Adaboost and the boosted LBP features are used to train a support vector machine that provides a recognition rate of 93.29%.