基于模糊逻辑的面部表情识别与推理

A. Ralescu, H. Iwamoto
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引用次数: 6

摘要

在面部图像的语言建模研究中,我们之前关注的是面部成分的定性描述,如“大眼睛,长发”。为了增强这个系统,我们扩展了我们的方法,以获得更高层次的定性描述。特别是,我们专注于描述面部表情。我们的方法是在模糊数建模的基础上进行定性建模。这种建模方法的结果是从输入输出数据中得到的模糊if-then规则集合。输入数据包括与不同面部表情相关的面部部分的运动测量。输出数据由使用问卷收集的面部图像的分数组成。在本文中,我们展示了该方法对“快乐”面部表情的建模结果。虽然建模结果是令人满意的,但最初的识别结果是有限的,部分原因是缺乏剩余面部表情的模型
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Recognition of and reasoning about facial expressions using fuzzy logic
In the study of the linguistic modeling of facial images we have been previously concerned with deriving qualitative descriptions such as "big eyes, long hair" of face components. To enhance this system we extend our approach at deriving higher level, qualitative descriptions. In particular, we focus on describing facial expressions. Our approach is that of qualitative modeling based on fuzzy number modeling. The result of this modeling method is a collection of fuzzy if-then rules obtained from input-output data. The input data consists of measurements of the movement of facial parts associated to different facial expressions. The output data consists of scores for face images collected using a questionnaire. In this paper, we show the modeling result obtained from this method for the facial expression "happy". While the modeling results are satisfactory the initial recognition results are limited, due in part to the absence of the models for the remaining facial expressions.<>
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