DocumentCode :
1799699
Title :
A semi-supervised temporal clustering method for facial emotion analysis
Author :
Araujo, Roberto ; Kamel, Mohamed S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada
fYear :
2014
fDate :
14-18 July 2014
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, we propose a semi-supervised temporal clustering method and apply it to the complex problem of facial emotion categorization. The proposed method, which uses a mechanism to add side information based on the semi-supervised kernel k-means framework, is an extension of the temporal clustering algorithm Aligned Cluster Analysis (ACA). We show that simply adding a small amount of soft constraints, in the form of must-link and cannot-link, improves the overall accuracy of the state-of-the-art method, ACA without adding any extra computational complexity. The results on the non-posed database VAM corpus for three different emotion primitives (valence, dominance, and activation) show improvements compared to the original approach.
Keywords :
emotion recognition; face recognition; pattern clustering; statistical analysis; ACA; aligned cluster analysis; facial emotion analysis; semisupervised kernel k-means framework; semisupervised temporal clustering method; Accuracy; Algorithm design and analysis; Clustering algorithms; Databases; Heuristic algorithms; Kernel; Linear programming; Clustering; Kernel k-means; Semi-supervised; Spontaneous facial expression; Temporal segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Expo Workshops (ICMEW), 2014 IEEE International Conference on
Conference_Location :
Chengdu
ISSN :
1945-7871
Type :
conf
DOI :
10.1109/ICMEW.2014.6890712
Filename :
6890712
Link To Document :
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