{"title":"Recognition of and reasoning about facial expressions using fuzzy logic","authors":"A. Ralescu, H. Iwamoto","doi":"10.1109/ROMAN.1993.367711","DOIUrl":null,"url":null,"abstract":"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.<<ETX>>","PeriodicalId":270591,"journal":{"name":"Proceedings of 1993 2nd IEEE International Workshop on Robot and Human Communication","volume":"30 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1993 2nd IEEE International Workshop on Robot and Human Communication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROMAN.1993.367711","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
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.<>