摘要
探讨了一个能够代表真实环境的数据集GENKI,构建笑脸分类系统,并采用支持向量机结合GentleBoost作为分类器.讨论了数据预处理、Gabor特征提取、PHOG特征提取和局部二值模式特征提取,给出了GENKI数据集上的实验结果并进行讨论,表明了该方法的有效性.
A smile expression classification system on data sets of GENKI is built which can represent real-world environments,and the support vector machine and GentleBoost algorithm are used as tools to learn.The followings are introduced： data preprocessing,Gabor features extraction,PHOG features extraction,and local binary pattern features extraction.The experiment results and detailed analysis of these results based on the GENKI dataset shows the effectiveness of the method.
出处
《华南师范大学学报（自然科学版）》
CAS
北大核心
2011年第2期49-55,共7页
Journal of South China Normal University(Natural Science Edition)
关键词
笑脸分类
GABOR滤波器组
金字塔分割
梯度方向直方图
局部二值模式
支持向量机
smile classification
Gabor filters bank
pyramid segmentation
histogram of oriented gradient
local binary pattern
support vector machine
