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Facial expression description and recognition based on fuzzy semantic concepts

机译：基于模糊语义概念的面部表达描述和识别

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This paper proposes a novel approach to identifying various expressions using semantic concepts. Based on the framework of the axiomatic fuzzy set, facial features are transformed into semantic concepts, which are then considered as a ruleset to differentiate expression categories. This method has two main advantages. First, it bridges the descriptors between image features and semantic concepts, according to which facial geometric features can be mirrored directly. Second, it alters the description patterns of fuzzy rulesets, which can reduce the dimensionality of expression features. We establish optimization criteria for selecting salient semantic concepts to represent expression characteristics. Experiments are conducted using the proposed method on the FEI and CK+ databases. Semantic concepts are considered as a ruleset to describe the differences between various expressions. The performances of state-of-the art classifiers and the proposed method are compared and analyzed. The results demonstrate that the proposed method provides excellent interpretability and classification performance for facial expressions.
机译：本文提出了一种使用语义概念识别各种表达的新方法。基于公理模糊集的框架，面部特征被转换为语义概念，然后被认为是分辨表达类别的规则集。该方法具有两个主要优点。首先，它遍历图像特征和语义概念之间的描述符，根据哪些面部几何特征直接镜像。其次，它改变了模糊规则集的描述模式，这可以降低表达特征的维度。我们建立了选择突出语义概念来表示表达特征的优化标准。使用FEI和CK +数据库上的所提出的方法进行实验。语义概念被视为规则集，以描述各种表达式之间的差异。比较和分析了最先进的分类器和所提出的方法的性能。结果表明，该方法为面部表情提供了出色的可解释性和分类性能。

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