基于融合特征的真实环境笑脸分类

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来学习.讨论了数据预处理,Gabor特征提取,PHOG特征提取,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详细讨论.实验表明了所给方法的有效性.

CLASSIFICATION OF SMILE EXPRESSION IN REAL WORLD ENVIRONMENT BASED ON FUSION FEATURES

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 are given. The empirical study on the GENKI dataset shows the effectiveness of the methods in this article.