A Fuzzy Reasoning Model for Recognition of Facial Expressions. Starostenko, O., Contreras, R., Alarcon-Aquino, V., Pulido, L., F., Asomoza, J., R., Sergiyenko, O., & Tyrsa, V. Computación y Sistemas, 15(2):163-180, 2011. ![A Fuzzy Reasoning Model for Recognition of Facial Expressions [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex In this paper we present a fuzzy reasoning model and a designed system for Recognition of Facial Expressions, which can measure and recognize the intensity of basic or non–prototypical emotions. The proposed model operates with encoded facial deformations described in terms of either Ekman's Action Units (AUs) or Facial Animation Parameters (FAPs) of MPEG–4 standard and provides recognition of facial expression using a knowledge base implemented on knowledge acquisition and ontology editor Protégé. It allows modeling of facial features obtained from geometric parameters coded by AUs – FAPs and from a set of rules required for classification of measured expressions. This paper also presents a designed framework for fuzzyfication of input variables of a fuzzy classifier based on statistical analysis of emotions expressed in video records of standard Cohn–Kanade's and Pantic's MMI face databases. The proposed system designed according to developed model has been tested in order to evaluate its capability for detection, indexing, classifying, and interpretation of facial expressions.
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abstract = {In this paper we present a fuzzy reasoning model and a designed system for Recognition of Facial Expressions, which can measure and recognize the intensity of basic or non–prototypical emotions. The proposed model operates with encoded facial deformations described in terms of either Ekman's Action Units (AUs) or Facial Animation Parameters (FAPs) of MPEG–4 standard and provides recognition of facial expression using a knowledge base implemented on knowledge acquisition and ontology editor Protégé. It allows modeling of facial features obtained from geometric parameters coded by AUs – FAPs and from a set of rules required for classification of measured expressions. This paper also presents a designed framework for fuzzyfication of input variables of a fuzzy classifier based on statistical analysis of emotions expressed in video records of standard Cohn–Kanade's and Pantic's MMI face databases. The proposed system designed according to developed model has been tested in order to evaluate its capability for detection, indexing, classifying, and interpretation of facial expressions.},
bibtype = {article},
author = {Starostenko, Oleg and Contreras, Renan and Alarcon-Aquino, V and Pulido, Leticia Flores and Asomoza, Jorge Rodriguez and Sergiyenko, Oleg and Tyrsa, Vira},
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