Authors:
Sonia Gharsalli
1
;
Hélène Laurent
2
;
Bruno Emile
1
and
Xavier Desquesnes
1
Affiliations:
1
Univ. Orl'eans, France
;
2
Academy of Strasbourg, France
Keyword(s):
Facial Emotion Recognition, Posed Expression, Spontaneous Expression, Early Fusion, Late Fusion, SVM, FEEDTUM Database, CK+ Database.
Abstract:
This paper investigates the performance of combining geometric features and appearance features with various fusion strategies in a facial emotion recognition application. Geometric features are extracted by a distance-based method; appearance features are extracted by a set of Gabor filters. Various fusion methods are proposed from two principal classes namely early fusion and late fusion. The former combines features in the feature space, the latter fuses both feature types in the decision space by a statistical rule or a classification method. Distance-based method, Gabor method and hybrid methods are evaluated on simulated (CK+) and spontaneous (FEEDTUM) databases. The comparison between methods shows that late fusion methods have better
recognition rates than the early fusion method. Moreover, late fusion methods based on statistical rules perform better than the other hybrid methods for simulated emotion recognition. However in the recognition of spontaneous emotions, the stati
stical-based methods improve the recognition of positive emotions, while the classification-based method slightly enhances sadness and disgust recognition. A comparison with hybrid methods from the literature is also made.
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