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.

本文提出了一种使用语义概念识别各种表达的新颖方法。基于公理模糊集的框架，将面部特征转换为语义概念，然后将其视为区分表情类别的规则集。此方法有两个主要优点。首先，它在图像特征和语义概念之间架起了描述符，从而可以直接镜像面部几何特征。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数。我们建立了用于选择表示表达特征的突出语义概念的优化标准。使用建议的方法在FEI和CK +数据库上进行了实验。语义概念被视为描述各种表达式之间差异的规则集。比较和分析了最新分类器的性能和提出的方法。结果表明，所提出的方法为面部表情提供了出色的解释性和分类性能。