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Title: Dynamic texture and geometry features for facial expression recognition in video
Authors: Chen, J
Chen, Z
Chi, Z 
Fu, H
Keywords: Facial expression recognition
Geometry features
Multiple kernel learning
Issue Date: 2015
Publisher: IEEE Computer Society
Source: Proceedings - International Conference on Image Processing, ICIP, 27-30 September 2015, 7351752, p. 4967-4971 How to cite?
Abstract: Facial expression recognition in video has attracted growing attention recently. In this paper, we propose to handle this problem with dynamic appearance and geometric features. We propose a new feature descriptor called HOG from Three Orthogonal Planes (HOG-TOP) to represent dynamic features. In addition, we introduce two types of geometry features to represent the facial rigid changes and non-rigid changes, respectively. Multiple Kernel Learning (MKL) is applied to find an optimal combination of two types of features. And finally a Support Vector Machine (SVM) with multiple kernels is trained for the facial expression classification. Extensive experiments conducted on the extended Cohn-Kanade dataset show that our method can achieve a competitive performance compared with the other state-of-the-art methods.
ISBN: 9781479983391
ISSN: 1522-4880
DOI: 10.1109/ICIP.2015.7351752
Appears in Collections:Conference Paper

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