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Facial expression classification using Support Vector Machine based on bidirectional Local Binary Pattern Histogram feature descriptor

机译:基于双向局部二值直方图特征描述符的支持向量机面部表情分类

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摘要

In this paper, two class emotion detection and multi class facial expression classification using Support Vector Machine (SVM) is presented. Facial feature vectors in dual form are obtained using Local Binary Pattern (LBP) Histogram by tracing the bins in clockwise and anticlockwise direction. The Histogram feature descriptors are calculated from LBP images in dual form which are then concatenated to obtain features of complete face image. The proposed algorithm is tested using standard Japanese Female Facial Expression Database and Taiwanese facial Expression Database and results are verified using locally developed Indian face database of students. The proposed algorithm significantly outperforms the classical LBP based algorithms.
机译:本文提出了使用支持向量机(SVM)进行两类情感检测和多类面部表情分类的方法。使用局部二进制模式(LBP)直方图,通过沿顺时针和逆时针方向跟踪垃圾箱,可以获取双重形式的面部特征向量。从双重形式的LBP图像计算直方图特征描述符,然后将其连接起来以获得完整的面部图像特征。使用标准的日本女性面部表情数据库和台湾面部表情数据库对提出的算法进行了测试,并使用本地开发的印度学生面部数据库验证了结果。所提出的算法明显优于传统的基于LBP的算法。

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