Active AU Based Patch Weighting for Facial Expression Recognition.
Sensors (Basel)
; 17(2)2017 Jan 30.
Article
em En
| MEDLINE
| ID: mdl-28146094
ABSTRACT
Facial expression has many applications in human-computer interaction. Although feature extraction and selection have been well studied, the speciï¬city of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on action unit (AU) weighting and patch weight optimization is proposed to represent the speciï¬city of the expression triplet. The sparse representation-based approach is then proposed to detect the active AUs of the testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for the Jaffe and Cohn-Kanade (CK+) databases, respectively. Better cross-database performance has also been observed.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
China