MQSMER: a mixed quadratic shape model with optimal fuzzy membership functions for emotion recognition

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020-04

Autores

R. Vishnu Priyaa (Autor) Outra; V. VijayaKumar (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Neural Computing and Applications

Vol. 32 Nº 7

Páginas: 3165-3182

ISSN: 0941-0643

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 0 Citações

ISI Web of Science

Scopus - 2 Citações

Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências da engenharia e tecnologias

Outras Informações

ID Authenticus: P-00Q-5SG

DOI: 10.1007/s00521-018-3940-0

Abstract (EN): The traditional geometrical-based approaches used in facial emotion recognition fail to capture the uncertainty present in the quadrilateral shape of emotions under analysis, which reduces the recognition accuracy rate. Furthermore, these approaches require extensive computational time to extract the facial features and to train the models. This article proposes a novel geometrical fuzzy-based approach to accurately recognize facial emotions in images in less time. The four corner vertices of the mouth are the most important features to recognize facial emotions and can be extracted without the need of a reference face. These extracted features can then be used to define the quadrilateral shape, and the associated degree of impreciseness in the shape can be accessed using the proposed geometric fuzzy membership functions. Hence, four fuzzy features are derived from the membership functions and given to classifiers for emotion evaluations. In our tests, the fuzzy features achieved an accuracy rate of 96.17% in the Japanese Female Facial Expression database, and 98.32% in the Cohn-Kanade Facial Expression database, which are higher than the ones achieved by other common up-to-date methods. In terms of computational time, the proposed method required an average of 0.375 s to build the used model in a common PC.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 18