A Model of Local Binary Pattern Feature Descriptor for Valence Facial Expression Classification

Ruth Agada, Jie Yan
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引用次数: 3

Abstract

Recognition of spontaneous emotion would significantly influence human-computer interaction and emotion-related studies in many related fields. This paper endeavors to explore a holistic method for detecting emotional facial expressions by examining local features. In recent years, examining local features has gained traction for nuanced expression detection. The local binary pattern is one such technique. Using the modified LBP adds a discriminating factor to the examined feature via the addition of an edge detector. Hence, the edge based local binary pattern for the extraction of features in the human face. Using this method, the extracted feature is classified into its valence classes (positive and negative) using an SVM classifier.
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一种用于价态表情分类的局部二值模式特征描述子模型
自发情绪的识别将对人机交互和许多相关领域的情绪相关研究产生重大影响。本文试图探索一种通过检测局部特征来检测面部情绪的整体方法。近年来,局部特征检测在细微差别表情检测中得到了广泛的应用。局部二进制模式就是这样一种技术。使用改进的LBP,通过添加边缘检测器,为被检测的特征增加了一个判别因子。因此,基于边缘的局部二值模式用于人脸特征的提取。利用该方法,利用SVM分类器将提取的特征分类为正价类和负价类。
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